text:
}
}

@inproceedings{10.1145/1390334.1390429,
author = {Zhang, Xiaodan and Hu, Xiaohua and Zhou, Xiaohua},
title = {A Comparative Evaluation of Different Link Types on Enhancing Document Clustering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390429},
doi = {10.1145/1390334.1390429},
abstract = {With a growing number of works utilizing link information in enhancing document clustering, it becomes necessary to make a comparative evaluation of the impacts of different link types on document clustering. Various types of links between text documents, including explicit links such as citation links and hyperlinks, implicit links such as co-authorship links, and pseudo links such as content similarity links, convey topic similarity or topic transferring patterns, which is very useful for document clustering. In this study, we adopt a Relaxation Labeling (RL)-based clustering algorithm, which employs both content and linkage information, to evaluate the effectiveness of the aforementioned types of links for document clustering on eight datasets. The experimental results show that linkage is quite effective in improving content-based document clustering. Furthermore, a series of interesting findings regarding the impacts of different link types on document clustering are discovered through our experiments.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {555–562},
numpages = {8},
keywords = {link-based clustering, relaxation labeling, markov random field},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390431,
author = {Theobald, Martin and Siddharth, Jonathan and Paepcke, Andreas},
title = {SpotSigs: Robust and Efficient near Duplicate Detection in Large Web Collections},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390431},
doi = {10.1145/1390334.1390431},
abstract = {Motivated by our work with political scientists who need to manually analyze large Web archives of news sites, we present SpotSigs, a new algorithm for extracting and matching signatures for near duplicate detection in large Web crawls. Our spot signatures are designed to favor natural-language portions of Web pages over advertisements and navigational bars.The contributions of SpotSigs are twofold: 1) by combining stopword antecedents with short chains of adjacent content terms, we create robust document signatures with a natural ability to filter out noisy components of Web pages that would otherwise distract pure n-gram-based approaches such as Shingling; 2) we provide an exact and efficient, self-tuning matching algorithm that exploits a novel combination of collection partitioning and inverted index pruning for high-dimensional similarity search. Experiments confirm an increase in combined precision and recall of more than 24 percent over state-of-the-art approaches such as Shingling or I-Match and up to a factor of 3 faster execution times than Locality Sensitive Hashing (LSH), over a demonstrative "Gold Set" of manually assessed near-duplicate news articles as well as the TREC WT10g Web collection.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {563–570},
numpages = {8},
keywords = {high-dimensional similarity search, inverted index pruning, stopword signatures, optimal partitioning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390432,
author = {Seo, Jangwon and Croft, W. Bruce},
title = {Local Text Reuse Detection},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390432},
doi = {10.1145/1390334.1390432},
abstract = {Text reuse occurs in many different types of documents and for many different reasons. One form of reuse, duplicate or near-duplicate documents, has been a focus of researchers because of its importance in Web search. Local text reuse occurs when sentences, facts or passages, rather than whole documents, are reused and modified. Detecting this type of reuse can be the basis of new tools for text analysis. In this paper, we introduce a new approach to detecting local text reuse and compare it to other approaches. This comparison involves a study of the amount and type of reuse that occurs in real documents, including TREC newswire and blog collections.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {571–578},
numpages = {8},
keywords = {information flow, text reuse, fingerprinting},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390433,
author = {Chen, Chien Chin and Chen, Meng Chang},
title = {TSCAN: A Novel Method for Topic Summarization and Content Anatomy},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390433},
doi = {10.1145/1390334.1390433},
abstract = {A topic is defined as a seminal event or activity along with all directly related events and activities. It is represented as a chronological sequence of documents by different authors published on the Internet. In this paper, we define a task called topic anatomy, which summarizes and associates core parts of a topic graphically so that readers can understand the content easily. The proposed topic anatomy model, called TSCAN, derives the major themes of a topic from the eigenvectors of a temporal block association matrix. Then, the significant events of the themes and their summaries are extracted by examining the constitution of the eigenvectors. Finally, the extracted events are associated through their temporal closeness and context similarity to form the evolution graph of the topic. Experiments based on the official TDT4 corpus demonstrate that the generated evolution graphs comprehensibly describe the storylines of topics. Moreover, in terms of content coverage and consistency, the produced summaries are superior to those of other summarization methods based on human composed reference summaries.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {579–586},
numpages = {8},
keywords = {topic anatomy. topic summarization, temporal text mining.},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390435,
author = {Yilmaz, Emine and Aslam, Javed A. and Robertson, Stephen},
title = {A New Rank Correlation Coefficient for Information Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390435},
doi = {10.1145/1390334.1390435},
abstract = {In the field of information retrieval, one is often faced with the problem of computing the correlation between two ranked lists. The most commonly used statistic that quantifies this correlation is Kendall's Τ. Often times, in the information retrieval community, discrepancies among those items having high rankings are more important than those among items having low rankings. The Kendall's Τ statistic, however, does not make such distinctions and equally penalizes errors both at high and low rankings.In this paper, we propose a new rank correlation coefficient, AP correlation (Τap), that is based on average precision and has a probabilistic interpretation. We show that the proposed statistic gives more weight to the errors at high rankings and has nice mathematical properties which make it easy to interpret. We further validate the applicability of the statistic using experimental data.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {587–594},
numpages = {8},
keywords = {rank correlation, evaluation, average precision, Kendall's tau},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390436,
author = {Druck, Gregory and Mann, Gideon and McCallum, Andrew},
title = {Learning from Labeled Features Using Generalized Expectation Criteria},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390436},
doi = {10.1145/1390334.1390436},
abstract = {It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled instances. Unlike previous approaches that use labeled features to create labeled pseudo-instances, we use labeled features directly to constrain the model's predictions on unlabeled instances. We express these soft constraints using generalized expectation (GE) criteria --- terms in a parameter estimation objective function that express preferences on values of a model expectation. In this paper we train multinomial logistic regression models using GE criteria, but the method we develop is applicable to other discriminative probabilistic models. The complete objective function also includes a Gaussian prior on parameters, which encourages generalization by spreading parameter weight to unlabeled features. Experimental results on text classification data sets show that this method outperforms heuristic approaches to training classifiers with labeled features. Experiments with human annotators show that it is more beneficial to spend limited annotation time labeling features rather than labeling instances. For example, after only one minute of labeling features, we can achieve 80% accuracy on the ibm vs. mac text classification problem using GE-FL, whereas ten minutes labeling documents results in an accuracy of only 77%},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {595–602},
numpages = {8},
keywords = {text classification, semi-supervised learning, labeled features, learning with domain knowledge},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390437,
author = {Yilmaz, Emine and Kanoulas, Evangelos and Aslam, Javed A.},
title = {A Simple and Efficient Sampling Method for Estimating AP and NDCG},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390437},
doi = {10.1145/1390334.1390437},
abstract = {We consider the problem of large scale retrieval evaluation. Recently two methods based on random sampling were proposed as a solution to the extensive effort required to judge tens of thousands of documents. While the first method proposed by Aslam et al. [1] is quite accurate and efficient, it is overly complex, making it difficult to be used by the community, and while the second method proposed by Yilmaz et al., infAP [14], is relatively simple, it is less efficient than the former since it employs uniform random sampling from the set of complete judgments. Further, none of these methods provide confidence intervals on the estimated values.The contribution of this paper is threefold: (1) we derive confidence intervals for infAP, (2) we extend infAP to incorporate nonrandom relevance judgments by employing stratified random sampling, hence combining the efficiency of stratification with the simplicity of random sampling, (3) we describe how this approach can be utilized to estimate nDCG from incomplete judgments. We validate the proposed methods using TREC data and demonstrate that these new methods can be used to incorporate nonrandom samples, as were available in TREC Terabyte track '06.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {603–610},
numpages = {8},
keywords = {evaluation, average precision, nDCG, incomplete judgments, infAP, sampling},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390438,
author = {Mei, Qiaozhu and Zhang, Duo and Zhai, ChengXiang},
title = {A General Optimization Framework for Smoothing Language Models on Graph Structures},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390438},
doi = {10.1145/1390334.1390438},
abstract = {Recent work on language models for information retrieval has shown that smoothing language models is crucial for achieving good retrieval performance. Many different effective smoothing methods have been proposed, which mostly implement various heuristics to exploit corpus structures. In this paper, we propose a general and unified optimization framework for smoothing language models on graph structures. This framework not only provides a unified formulation of the existing smoothing heuristics, but also serves as a road map for systematically exploring smoothing methods for language models. We follow this road map and derive several different instantiations of the framework. Some of the instantiations lead to novel smoothing methods. Empirical results show that all such instantiations are effective with some outperforming the state of the art smoothing methods.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {611–618},
numpages = {8},
keywords = {smoothing, document and word graph, graph structure, Language modeling},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390440,
author = {Xue, Gui-Rong and Xing, Dikan and Yang, Qiang and Yu, Yong},
title = {Deep Classification in Large-Scale Text Hierarchies},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390440},
doi = {10.1145/1390334.1390440},
abstract = {Most classification algorithms are best at categorizing the Web documents into a few categories, such as the top two levels in the Open Directory Project. Such a classification method does not give very detailed topic-related class information for the user because the first two levels are often too coarse. However, classification on a large-scale hierarchy is known to be intractable for many target categories with cross-link relationships among them. In this paper, we propose a novel deep-classification approach to categorize Web documents into categories in a large-scale taxonomy. The approach consists of two stages: a search stage and a classification stage. In the first stage, a category-search algorithm is used to acquire the category candidates for a given document. Based on the category candidates, we prune the large-scale hierarchy to focus our classification effort on a small subset of the original hierarchy. As a result, the classification model is trained on the small subset before being applied to assign the category for a new document. Since the category candidates are sufficiently close to each other in the hierarchy, a statistical-language-model based classifier using n-gram features is exploited. Furthermore, the structure of the taxonomy can be utilized in this stage to improve the performance of classification. We demonstrate the performance of our proposed algorithms on the Open Directory Project with over 130,000 categories. Experimental results show that our proposed approach can reach 51.8% on the measure of Mi-F1 at the 5th level, which is 77.7% improvement over top-down based SVM classification algorithms.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {619–626},
numpages = {8},
keywords = {large scale hierarchy, deep classification, hierarchical classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390441,
author = {Xue, Gui-Rong and Dai, Wenyuan and Yang, Qiang and Yu, Yong},
title = {Topic-Bridged PLSA for Cross-Domain Text Classification},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390441},
doi = {10.1145/1390334.1390441},
abstract = {In many Web applications, such as blog classification and new-sgroup classification, labeled data are in short supply. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data in a related but different domain. Traditional text classification ap-proaches are not able to cope well with learning across different domains. In this paper, we propose a novel cross-domain text classification algorithm which extends the traditional probabilistic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled data, which come from different but related domains, into a unified probabilistic model. We call this new model Topic-bridged PLSA, or TPLSA. By exploiting the common topics between two domains, we transfer knowledge across different domains through a topic-bridge to help the text classification in the target domain. A unique advantage of our method is its ability to maximally mine knowledge that can be transferred between domains, resulting in superior performance when compared to other state-of-the-art text classification approaches. Experimental eval-uation on different kinds of datasets shows that our proposed algorithm can improve the performance of cross-domain text classification significantly.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {627–634},
numpages = {8},
keywords = {topic-bridged PLSA, text classification, cross-domain},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390442,
author = {Yu, Kai and Zhu, Shenghuo and Xu, Wei and Gong, Yihong},
title = {TrNon-Greedy Active Learning for Text Categorization Using Convex Ansductive Experimental Design},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390442},
doi = {10.1145/1390334.1390442},
abstract = {In this paper we propose a non-greedy active learning method for text categorization using least-squares support vector machines (LSSVM). Our work is based on transductive experimental design (TED), an active learning formulation that effectively explores the information of unlabeled data. Despite its appealing properties, the optimization problem is however NP-hard and thus--like most of other active learning methods--a greedy sequential strategy to select one data example after another was suggested to find a suboptimum. In this paper we formulate the problem into a continuous optimization problem and prove its convexity, meaning that a set of data examples can be selected with a guarantee of global optimum. We also develop an iterative algorithm to efficiently solve the optimization problem, which turns out to be very easy-to-implement. Our text categorization experiments on two text corpora empirically demonstrated that the new active learning algorithm outperforms the sequential greedy algorithm, and is promising for active text categorization applications.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {635–642},
numpages = {8},
keywords = {transductive experimental design, active learning, convex optimization, text categorization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390443,
author = {Qi, Xiaoguang and Davison, Brian D.},
title = {Classifiers without Borders: Incorporating Fielded Text from Neighboring Web Pages},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390443},
doi = {10.1145/1390334.1390443},
abstract = {Accurate web page classification often depends crucially on information gained from neighboring pages in the local web graph. Prior work has exploited the class labels of nearby pages to improve performance. In contrast, in this work we utilize a weighted combination of the contents of neighbors to generate a better virtual document for classification. In addition, we break pages into fields, finding that a weighted combination of text from the target and fields of neighboring pages is able to reduce classification error by more than a third. We demonstrate performance on a large dataset of pages from the Open Directory Project and validate the approach using pages from a crawl from the Stanford WebBase. Interestingly, we find no value in anchor text and unexpected value in page titles (and especially titles of parent pages) in the virtual document.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {643–650},
numpages = {8},
keywords = {SVM, naive Bayes, neighboring, web page classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390445,
author = {Carterette, Ben and Pavlu, Virgil and Kanoulas, Evangelos and Aslam, Javed A. and Allan, James},
title = {Evaluation over Thousands of Queries},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390445},
doi = {10.1145/1390334.1390445},
abstract = {Information retrieval evaluation has typically been performed over several dozen queries, each judged to near-completeness. There has been a great deal of recent work on evaluation over much smaller judgment sets: how to select the best set of documents to judge and how to estimate evaluation measures when few judgments are available. In light of this, it should be possible to evaluate over many more queries without much more total judging effort. The Million Query Track at TREC 2007 used two document selection algorithms to acquire relevance judgments for more than 1,800 queries. We present results of the track, along with deeper analysis: investigating tradeoffs between the number of queries and number of judgments shows that, up to a point, evaluation over more queries with fewer judgments is more cost-effective and as reliable as fewer queries with more judgments. Total assessor effort can be reduced by 95% with no appreciable increase in evaluation errors.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {651–658},
numpages = {8},
keywords = {test collections, information retrieval, million query track, evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390446,
author = {Clarke, Charles L.A. and Kolla, Maheedhar and Cormack, Gordon V. and Vechtomova, Olga and Ashkan, Azin and B\"{u}ttcher, Stefan and MacKinnon, Ian},
title = {Novelty and Diversity in Information Retrieval Evaluation},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390446},
doi = {10.1145/1390334.1390446},
abstract = {Evaluation measures act as objective functions to be optimized by information retrieval systems. Such objective functions must accurately reflect user requirements, particularly when tuning IR systems and learning ranking functions. Ambiguity in queries and redundancy in retrieved documents are poorly reflected by current evaluation measures. In this paper, we present a framework for evaluation that systematically rewards novelty and diversity. We develop this framework into a specific evaluation measure, based on cumulative gain. We demonstrate the feasibility of our approach using a test collection based on the TREC question answering track.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {659–666},
numpages = {8},
keywords = {test collections, novelty, evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390447,
author = {Bailey, Peter and Craswell, Nick and Soboroff, Ian and Thomas, Paul and de Vries, Arjen P. and Yilmaz, Emine},
title = {Relevance Assessment: Are Judges Exchangeable and Does It Matter},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390447},
doi = {10.1145/1390334.1390447},
abstract = {We investigate to what extent people making relevance judgements for a reusable IR test collection are exchangeable. We consider three classes of judge: "gold standard" judges, who are topic originators and are experts in a particular information seeking task; "silver standard" judges, who are task experts but did not create topics; and "bronze standard" judges, who are those who did not define topics and are not experts in the task.Analysis shows low levels of agreement in relevance judgements between these three groups. We report on experiments to determine if this is sufficient to invalidate the use of a test collection for measuring system performance when relevance assessments have been created by silver standard or bronze standard judges. We find that both system scores and system rankings are subject to consistent but small differences across the three assessment sets. It appears that test collections are not completely robust to changes of judge when these judges vary widely in task and topic expertise. Bronze standard judges may not be able to substitute for topic and task experts, due to changes in the relative performance of assessed systems, and gold standard judges are preferred.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {667–674},
numpages = {8},
keywords = {inter-rater agreement, test collection relevance judgements},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390448,
author = {Keskustalo, Heikki and J\"{a}rvelin, Kalervo and Pirkola, Ari and Kek\"{a}l\"{a}inen, Jaana},
title = {Intuition-Supporting Visualization of User's Performance Based on Explicit Negative Higher-Order Relevance},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390448},
doi = {10.1145/1390334.1390448},
abstract = {Modeling the beyond-topical aspects of relevance are currently gaining popularity in IR evaluation. For example, the discounted cumulated gain (DCG) measure implicitly models some aspects of higher-order relevance via diminishing the value of relevant documents seen later during retrieval (e.g., due to information cumulated, redundancy, and effort). In this paper, we focus on the concept of negative higher-order relevance (NHOR) made explicit via negative gain values in IR evaluation. We extend the computation of DCG to allow negative gain values, perform an experiment in a laboratory setting, and demonstrate the characteristics of NHOR in evaluation. The approach leads to intuitively reasonable performance curves emphasizing, from the user's point of view, the progression of retrieval towards success or failure. We discuss normalization issues when both positive and negative gain values are allowed and conclude by discussing the usage of NHOR to characterize test collections.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {675–682},
numpages = {8},
keywords = {relevance, evaluation, discounted cumulated gain, test collections},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390450,
author = {Al-Maskari, Azzah and Sanderson, Mark and Clough, Paul},
title = {Relevance Judgments between TREC and Non-TREC Assessors},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390450},
doi = {10.1145/1390334.1390450},
abstract = {This paper investigates the agreement of relevance assessments between official TREC judgments and those generated from an interactive IR experiment. Results show that 63% of documents judged relevant by our users matched official TREC judgments. Several factors contributed to differences in the agreements: the number of retrieved relevant documents; the number of relevant documents judged; system effectiveness per topic and the ranking of relevant documents.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {683–684},
numpages = {2},
keywords = {user study, TREC relevance assessment},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390451,
author = {Carterette, Ben and Bennett, Paul N.},
title = {Evaluation Measures for Preference Judgments},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390451},
doi = {10.1145/1390334.1390451},
abstract = {There has been recent interest in collecting user or assessor preferences, rather than absolute judgments of relevance, for the evaluation or learning of ranking algorithms. Since measures like precision, recall, and DCG are defined over absolute judgments, evaluation over preferences will require new evaluation measures that explicitly model them. We describe a class of such measures and compare absolute and preference measures over a large TREC collection.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {685–686},
numpages = {2},
keywords = {preference judgments, evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390452,
author = {Ravana, Sri Devi and Moffat, Alistair},
title = {Exploring Evaluation Metrics: GMAP versus MAP},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390452},
doi = {10.1145/1390334.1390452},
abstract = {In retrieval experiments, an effectiveness metrics is used to generate a score for each system-topic pair being tested. It is then usual to average the system-topic scores to obtain a system score, which is used for the purpose of system comparison. In this paper we explore the ramifications of using the geometric mean (GMAP), rather than the arithmetic mean (MAP) when computing an aggregate system score from a set of system-topic scores. We find that GMAP does indeed handle variability in topic difficulty more consistently than does the usual MAP aggregation method.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {687–688},
numpages = {2},
keywords = {average precision, gmap, map, evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390453,
author = {Robertson, Stephen},
title = {A New Interpretation of Average Precision},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390453},
doi = {10.1145/1390334.1390453},
abstract = {We consider the question of whether Average Precision, as a measure of retrieval effectiveness, can be regarded as deriving from a model of user searching behaviour. It turns out that indeed it can be so regarded, under a very simple stochastic model of user behaviour.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {689–690},
numpages = {2},
keywords = {MAP, average precision, IR evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390454,
author = {Sakai, Tetsuya},
title = {Comparing Metrics across TREC and NTCIR: The Robustness to Pool Depth Bias},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390454},
doi = {10.1145/1390334.1390454},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {691–692},
numpages = {2},
keywords = {test collection, evaluation metrics, graded relevance},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390455,
author = {Scholer, Falk and Turpin, Andrew},
title = {Relevance Thresholds in System Evaluations},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390455},
doi = {10.1145/1390334.1390455},
abstract = {We introduce and explore the concept of an individual's relevance threshold as a way of reconciling differences in outcomes between batch and user experiments.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {693–694},
numpages = {2},
keywords = {search engines, user study, information retrieval evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390456,
author = {Webber, William and Moffat, Alistair and Zobel, Justin and Sakai, Tetsuya},
title = {Precision-at-Ten Considered Redundant},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390456},
doi = {10.1145/1390334.1390456},
abstract = {Information retrieval systems are compared using evaluation metrics, with researchers commonly reporting results for simple metrics such as precision-at-10 or reciprocal rank together with more complex ones such as average precision or discounted cumulative gain. In this paper, we demonstrate that complex metrics are as good as or better than simple metrics at predicting the performance of the simple metrics on other topics. Therefore, reporting of results from simple metrics alongside complex ones is redundant.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {695–696},
numpages = {2},
keywords = {evaluation, system measurement, retrieval experiment},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390457,
author = {Alonso, Omar and Talbot, Justin},
title = {Structuring Collections with Scatter/Gather Extensions},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390457},
doi = {10.1145/1390334.1390457},
abstract = {A major component of sense-making is organizing--grouping, labeling, and summarizing--the data at hand in order to form a useful mental model, a necessary precursor to identifying missing information and to reasoning about the data. Previous work has shown the Scatter/Gather model to be useful in exploratory activities that occur when users encounter unknown document collections. However, the topic structure communicated by Scatter/Gather is closely tied to the behavior of the underlying clustering algorithm; this structure may not reflect the mental model most applicable to the information need. In this paper we describe the initial design of a mixed-initiative information structuring tool that leverages aspects of the well-studied Scatter/Gather model but permits the user to impose their own desired structure when necessary.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {697–698},
numpages = {2},
keywords = {clustering, text summarization, sense-making, Scatter/Gather},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390458,
author = {Majumder, Prasenjit and Mitra, Mandar and Pal, Dipasree and Bandyopadhyay, Ayan and Maiti, Samaresh and Mitra, Sukanya and Sen, Aparajita and Pal, Sukomal},
title = {Text Collections for FIRE},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390458},
doi = {10.1145/1390334.1390458},
abstract = {The aim of the Forum for Information Retrieval Evaluation (FIRE) is to create a Cranfield-like evaluation framework in the spirit of TREC, CLEF and NTCIR, for Indian Language Information Retrieval. For the first year, six Indian languages have been selected: Bengali, Hindi, Marathi, Punjabi, Tamil, and Telugu. This poster describes the tasks as well as the document and topic collections that are to be used at the FIRE workshop.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {699–700},
numpages = {2},
keywords = {evaluation, fire, indian language information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390459,
author = {Anick, Peter and Kantamneni, Raj Gopal},
title = {A Longitudinal Study of Real-Time Search Assistance Adoption},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390459},
doi = {10.1145/1390334.1390459},
abstract = {We present findings from a log based study designed to track the adoption of features of a new real-time query refinement interface deployed on the Yahoo search engine. Several trends from the first four months are noted and discussed.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {701–702},
numpages = {2},
keywords = {search assistance, query refinement, web search, user interfaces},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390460,
author = {Berlocher, Ivan and Lee, Kyung-il and Kim, Kono},
title = {TopicRank: Bringing Insight to Users},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390460},
doi = {10.1145/1390334.1390460},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {703–704},
numpages = {2},
keywords = {word clustering, automated annotation, tag clouds},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390461,
author = {Bilenko, Mikhail and White, Ryen W. and Richardson, Matthew and Murray, G. Craig},
title = {Talking the Talk vs. Walking the Walk: Salience of Information Needs in Querying vs. Browsing},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390461},
doi = {10.1145/1390334.1390461},
abstract = {Traditional information retrieval models assume that users express their information needs via text queries (i.e., their "talk"). In this poster, we consider Web browsing behavior outside of interactions with retrieval systems (i.e., users' "walk") as an alternative source of signal describing users' information needs, and compare it to the query-expressed information needs on a large dataset. Our findings demonstrate that information needs expressed in different behavior modalities are largely non-overlapping, and that past behavior in each modality is the most accurate predictor of future behavior in that modality. Results also show that browsing data provides a stronger source of signal than search queries due to its greater volume, which explains previous work that has found implicit behavioral data to be a valuable source of information for user modeling and personalization.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {705–706},
numpages = {2},
keywords = {personalization, predictive user modeling, user profiles},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390462,
author = {Guo, Qi and Agichtein, Eugene},
title = {Exploring Mouse Movements for Inferring Query Intent},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390462},
doi = {10.1145/1390334.1390462},
abstract = {Clickthrough on search results have been successfully used to infer user interest and preferences, but are often noisy and potentially ambiguous. We explore the potential of a complementary, more sensitive signal -mouse movements- in providing insights into the intent behind a web search query. We report preliminary results of studying user mouse movements on search result pages, with the goal of inferring user intent - in particular, to explore whether we can automatically distinguish the different query classes such as navigational vs. informational. Our preliminary exploration confirms the value of studying mouse movements for user intent inference, and suggests interesting avenues for future exploration.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {707–708},
numpages = {2},
keywords = {user behavior modeling, mouse movement analysis, query intent inference},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390463,
author = {Joho, Hideo and Hannah, David and Jose, Joemon M.},
title = {Emulating Query-Biased Summaries Using Document Titles},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390463},
doi = {10.1145/1390334.1390463},
abstract = {Generating query-biased summaries can take up a large part of the response time of interactive information retrieval (IIR) systems. This paper proposes to use document titles as an alternative to queries in the generation of summaries. The use of document titles allows us to pre-generate summaries statically, and thus, improve the response speed of IIR systems. Our experiments suggest that title-biased summaries are a promising alternative to query-biased summaries.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {709–710},
numpages = {2},
keywords = {title-biased summaries, interactive information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390464,
author = {Huete, J. F. and de Campos, L. M. and Fernandez-Luna, J. M. and Rueda-Morales, M. A.},
title = {Hierarchical Naive Bayes Models for Representing User Profiles},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390464},
doi = {10.1145/1390334.1390464},
abstract = {In this paper, we show how a user profile can be enhanced when a more detailed description of the products is included. Two main assumptions have been considered: the first implies that the set of features used to describe an item can be organized into a well-defined set of components or categories, and the second is that the user's rating for a given item is obtained by combining user opinions of the relevance of each component.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {711–712},
numpages = {2},
keywords = {learning user profiles, recommender system, probabilistic reasoning, recommender systems},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390465,
author = {Zhang, Liyan and Zhang, Kai and Li, Chunping},
title = {A Topical PageRank Based Algorithm for Recommender Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390465},
doi = {10.1145/1390334.1390465},
abstract = {In this paper, we propose a Topical PageRank based algorithm for recommender systems, which aim to rank products by analyzing previous user-item relationships, and recommend top-rank items to potentially interested users. We evaluate our algorithm on MovieLens dataset and empirical experiments demonstrate that it outperforms other state-of-the-art recommending algorithms.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {713–714},
numpages = {2},
keywords = {recommender system, topical PageRank},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390466,
author = {Zhu, Yangbo and Callan, Jamie and Carbonell, Jaime},
title = {The Impact of History Length on Personalized Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390466},
doi = {10.1145/1390334.1390466},
abstract = {Personalized search is a promising way to better serve different users' information needs. Search history is one of the major information sources for search personalization. We investigated the impact of history length on the effectiveness of personalized ranking. We carried out task-based user study for Web search, and obtained ranked relevance judgments for all queries. Query contexts derived from previous queries in the same task are used to re-rank results for the current query. Experimental results show that the performance of personalization generally improves as more queries are accumulated, but most of the benefits come from a few immediately preceding queries.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {715–716},
numpages = {2},
keywords = {evaluation, web search, personalized search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390467,
author = {Wu, Mingfang and Scholer, Falk and Turpin, Andrew},
title = {User Preference Choices for Complex Question Answering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390467},
doi = {10.1145/1390334.1390467},
abstract = {Question answering systems increasingly need to deal with complex information needs that require more than simple factoid answers. The evaluation of such systems is usually carried out using precision- or recall-based system performance metrics. Previous work has demonstrated that when users are shown two search result lists side-by-side, they can reliably differentiate between the qualities of the lists. We investigate the consistency between this user-based approach and system-oriented metrics in the question answering environment. Our initial results indicate that the two methodologies show a high level of disagreement.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {717–718},
numpages = {2},
keywords = {ciQA, human preference and judgement, TREC},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390468,
author = {Carman, Mark J. and Crestani, Fabio},
title = {Towards Personalized Distributed Information Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390468},
doi = {10.1145/1390334.1390468},
abstract = {Our aim is to investigate if and how the performance of Distributed Information Retrieval (DIR) systems can be improved through personalization. Toward this aim we are building a testbed of document collections and corresponding personalized relevance judgments. In this paper we discuss our intended approach for personalizing the three different phases of the DIR process. We also describe the test collection we are building and discuss our methodology for evaluating personalized DIR using relevance information taken from social bookmarking data.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {719–720},
numpages = {2},
keywords = {personalization, distributed information retrieval, evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390469,
author = {Luxenburger, Julia and Elbassuoni, Shady and Weikum, Gerhard},
title = {Task-Aware Search Personalization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390469},
doi = {10.1145/1390334.1390469},
abstract = {Search personalization has been pursued in many ways, in order to provide better result rankings and better overall search experience to individual users [5]. However, blindly applying personalization to all user queries, for example, by a background model derived from the user's long-term query-and-click history, is not always appropriate for aiding the user in accomplishing her actual task. User interests change over time, a user sometimes works on very different categories of tasks within a short timespan, and history-based personalization may impede a user's desire of discovering new topics. In this paper we propose a personalization framework that is selective in a twofold sense. First, it selectively employs personalization techniques for queries that are expected to benefit from prior history information, while refraining from undue actions otherwise. Second, we introduce the notion of tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics, and base our reasoning selectively on query-relevant user tasks. These considerations are cast into a statistical language model for tasks, queries, and documents, supporting both judicious query expansion and result re-ranking. The effectiveness of our method is demonstrated by an empirical user study.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {721–722},
numpages = {2},
keywords = {language model, user tasks, selective personalization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390471,
author = {Elgersma, Erik and de Rijke, Maarten},
title = {Personal vs Non-Personal Blogs: Initial Classification Experiments},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390471},
doi = {10.1145/1390334.1390471},
abstract = {We address the task of separating personal from non-personal blogs, and report on a set of baseline experiments where we compare the performance on a small set of features across a set of five classifiers. We show that with a limited set of features a performance of up to 90% can be obtained.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {723–724},
numpages = {2},
keywords = {language modeling, blog classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390472,
author = {Jiang, Maojin and Argamon, Shlomo},
title = {Exploiting Subjectivity Analysis in Blogs to Improve Political Leaning Categorization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390472},
doi = {10.1145/1390334.1390472},
abstract = {In this paper, we address a relatively new and interesting text categorization problem: classify a political blog as either liberal or conservative, based on its political leaning. Our subjectivity analysis based method is twofold: 1) we identify subjective sentences that contain at least two strong subjective clues based on the General Inquirer dictionary; 2) from subjective sentences identified, we extract opinion expressions and other features to build political leaning classifiers. Experimental results with a political blog corpus we built show that by using features from subjective sentences can significantly improve the classification performance. In addition, by extracting opinion expressions from subjective sentences, we are able to reveal opinions that are characteristic of a specific political leaning to some extent.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {725–726},
numpages = {2},
keywords = {subjectivity analysis, political leaning categorization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390473,
author = {He, Ben and Macdonald, Craig and Ounis, Iadh},
title = {Ranking Opinionated Blog Posts Using OpinionFinder},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390473},
doi = {10.1145/1390334.1390473},
abstract = {The aim of an opinion finding system is not just to retrieve relevant documents, but to also retrieve documents that express an opinion towards the query target entity. In this work, we propose a way to use and integrate an opinion-identification toolkit, OpinionFinder, into the retrieval process of an Information Retrieval (IR) system, such that opinionated, relevant documents are retrieved in response to a query. In our experiments, we vary the number of top-ranked documents that must be parsed in response to a query, and investigate the effect on opinion retrieval performance and required parsing time. We find that opinion finding retrieval performance is improved by integrating OpinionFinder into the retrieval system, and that retrieval performance grows as more posts are parsed by OpinionFinder. However, the benefit eventually tails off at a deep rank, suggesting that an optimal setting for the system has been achieved.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {727–728},
numpages = {2},
keywords = {blogs, opinion finding},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390474,
author = {Sun, Aixin and Hu, Meishan and Lim, Ee-Peng},
title = {Searching Blogs and News: A Study on Popular Queries},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390474},
doi = {10.1145/1390334.1390474},
abstract = {Blog/news search engines are very important channels to reach information about the real-time happenings. In this paper, we study the popular queries collected over one year period and compare their search results returned by a blog search engine (i.e., Technorati) and a news search engine (i.e., Google News). We observed that the numbers of hits returned by the two search engines for the same set of queries were highly correlated, suggesting that blogs often provide commentary to current events reported in news. As many popular queries are related to some events, we further observed a high cohesiveness among the returned search results for these queries.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {729–730},
numpages = {2},
keywords = {blog, news, search engine, popular queries, search results},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390475,
author = {Wu, Mingfang and Turpin, Andrew and Zobel, Justin},
title = {Aggregated Click-through Data in a Homogeneous User Community},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390475},
doi = {10.1145/1390334.1390475},
abstract = {There are many proposed methods for using clickthrough data for common queries to improve the quality of search results returned for that query. In this study we examine the search behaviour of users in a close-knit community on such queries. We argue that the benefit of using aggregated clickthrough data varies from task to task: it may improve document rankings for navigational or specific informational queries, but is less likely to be of value to users issuing a broad informational query.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {731–732},
numpages = {2},
keywords = {search variability, information need, query logs},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390476,
author = {Budura, Adriana and Michel, Sebastian and Cudr\'{e}-Mauroux, Philippe and Aberer, Karl},
title = {To Tag or Not to Tag -: Harvesting Adjacent Metadata in Large-Scale Tagging Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390476},
doi = {10.1145/1390334.1390476},
abstract = {We present HAMLET, a suite of principles, scoring models and algorithms to automatically propagate metadata along edges in a document neighborhood. As a showcase scenario we consider tag prediction in community-based Web 2.0 tagging applications. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce. To the best of our knowledge, HAMLET is the first system to promote an efficient and precise reuse of shared metadata in highly dynamic, large-scale Web 2.0 tagging systems.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {733–734},
numpages = {2},
keywords = {tagging systems, tag propagation, social communities},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390477,
author = {Li, Baoli and Liu, Yandong and Ram, Ashwin and Garcia, Ernest V. and Agichtein, Eugene},
title = {Exploring Question Subjectivity Prediction in Community QA},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390477},
doi = {10.1145/1390334.1390477},
abstract = {In this paper we begin to investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {735–736},
numpages = {2},
keywords = {question classification, subjectivity analysis},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390478,
author = {Liu, Yandong and Agichtein, Eugene},
title = {On the Evolution of the Yahoo! Answers QA Community},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390478},
doi = {10.1145/1390334.1390478},
abstract = {While question answering communities have been gaining popularity for several years, we wonder if the increased popularity actually improves or degrades the user experience. In addition, automatic QA systems, which utilize different sources such as search engines and social media, are emerging rapidly. QA communities have already created abundant resources of millions of questions and hundreds of millions of answers. The question whether they will continue to serve as an effective source is of information for web search and question answering is of vital importance. In this poster, we investigate the temporal evolution of a popular QA community - Yahoo! Answers, with respect to its effectiveness in answering three basic types of questions: factoid, opinion and complex questions. Our experiments show that Yahoo! Answers keeps growing rapidly, while its overall quality as an information source for factoid question-answering degrades. However, instead of answering factoid questions, it might be more effective to answer opinion and complex questions.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {737–738},
numpages = {2},
keywords = {question answering, social media},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390479,
author = {Clements, Maarten and de Vries, Arjen P. and Reinders, Marcel J.T.},
title = {Detecting Synonyms in Social Tagging Systems to Improve Content Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390479},
doi = {10.1145/1390334.1390479},
abstract = {Collaborative tagging used in online social content systems is naturally characterized by many synonyms, causing low precision retrieval. We propose a mechanism based on user preference profiles to identify synonyms that can be used to retrieve more relevant documents by expanding the user's query. Using a popular online book catalog we discuss the effectiveness of our method over usual similarity based expansion methods.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {739–740},
numpages = {2},
keywords = {content retrieval, synonyms, social media, tagging},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390480,
author = {Zhou, Chao and Qiu, Guang and Liu, Kangmiao and Bu, Jiajun and Qu, Mingcheng and Chen, Chun},
title = {SOPING: A Chinese Customer Review Mining System},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390480},
doi = {10.1145/1390334.1390480},
abstract = {With the booming development of the Web, popular Chinese forums enable people to find experienced customers' reviews for products. In order to get an all-around opinion about one product, users need to go through plenty of web pages, which is time-consuming and inefficient. Consequently, automatic review mining and summarization has become a hot research topic recently. However, previous approaches are not applicable for mining Chinese customer reviews. In this paper, we introduce SOPING, a Chinese customer review mining system that mines reviews from forums. Specifically, we propose a novel search-based approach to extract product features and a feature-oriented sentence orientation determination method. Our experimental results show that our proposed techniques are highly effective.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {741–742},
numpages = {2},
keywords = {search, user input, chinese reviews, sentiment classification, feature extraction},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390481,
author = {Tan, Songbo and Wang, Yuefen and Cheng, Xueqi},
title = {Combining Learn-Based and Lexicon-Based Techniques for Sentiment Detection without Using Labeled Examples},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390481},
doi = {10.1145/1390334.1390481},
abstract = {In this work, we propose a novel scheme for sentiment classification (without labeled examples) which combines the strengths of both "learn-based" and "lexicon-based" approaches as follows: we first use a lexicon-based technique to label a portion of informative examples from given task (or domain); then learn a new supervised classifier based on these labeled ones; finally apply this classifier to the task. The experimental results indicate that proposed scheme could dramatically outperform "learn-based" and "lexicon-based" techniques.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {743–744},
numpages = {2},
keywords = {opinion mining, sentiment classification; opinion mining, information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390482,
author = {Mojdeh, Mona and Cormack, Gordon V.},
title = {Semi-Supervised Spam Filtering: Does It Work?},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390482},
doi = {10.1145/1390334.1390482},
abstract = {The results of the 2006 ECML/PKDD Discovery Challenge suggest that semi-supervised learning methods work well for spam filtering when the source of available labeled examples differs from those to be classified. We have attempted to reproduce these results using data from the 2005 and 2007 TREC Spam Track, and have found the opposite effect: methods like self-training and transductive support vector machines yield inferior classifiers to those constructed using supervised learning on the labeled data alone. We investigate differences between the ECML/PKDD and TREC data sets and methodologies that may account for the opposite results.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {745–746},
numpages = {2},
keywords = {self-feedback, transductive learning, spam, semi-supervised learning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390483,
author = {Macdonald, Craig and He, Ben and Ounis, Iadh and Soboroff, Ian},
title = {Limits of Opinion-Finding Baseline Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390483},
doi = {10.1145/1390334.1390483},
abstract = {In opinion-finding, the retrieval system is tasked with retrieving not just relevant documents, but which also express an opinion towards the query target entity. Most opinion-finding systems are based on a two-stage approach, where initially the system aims to retrieve relevant documents, which are then re-ranked according to the extent to which they are detected to be of an opinionated nature. In this work, we investigate how the underlying 'baseline' retrieval system performance affects the overall opinion-finding performance. We apply two effective opinion-finding techniques to all the baseline runs submitted to the TREC 2007 Blog track, and draw new insights and conclusions.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {747–748},
numpages = {2},
keywords = {opinion finding, baselines},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390484,
author = {Hu, Rong and Chen, Weizhu and Bai, Peng and Lu, Yansheng and Chen, Zheng and Yang, Qiang},
title = {Web Query Translation via Web Log Mining},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390484},
doi = {10.1145/1390334.1390484},
abstract = {This paper describes a method to automatically acquire query translation pairs by mining web click-through data. The extraction requires no crawling or Chinese words segmentation, and can capture popular translations. Experimental results on a real click-through data show that only 17.4% of the extracted queries are in the dictionary, and our method can achieve 62.2% (in top-1) to 80.0% (in top-5) precision in translating web queries. Moreover, the extracted translations are semantically relevant to the source query, which is particularly useful for Cross-Lingual Information Retrieval (CLIR).},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {749–750},
numpages = {2},
keywords = {query translation, cross-lingual IR, click-through log mining},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390485,
author = {Wei, Xing and Peng, Fuchun and Dumoulin, Benoit},
title = {Analyzing Web Text Association to Disambiguate Abbreviation in Queries},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390485},
doi = {10.1145/1390334.1390485},
abstract = {We introduce a statistical model for abbreviation disambiguation in Web search, based on analysis of Web data resources, including anchor text, click log and query log. By combining evidence from multiple sources, we are able to accurately disambiguate the abbreviation in queries. Experiments on real Web search queries show promising results.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {751–752},
numpages = {2},
keywords = {abbreviation, query expansion, text association, web search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390486,
author = {Balog, Krisztian and de Rijke, Maarten and Weerkamp, Wouter},
title = {Bloggers as Experts: Feed Distillation Using Expert Retrieval Models},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390486},
doi = {10.1145/1390334.1390486},
abstract = {We address the task of (blog) feed distillation: to find blogs that are principally devoted to a given topic. The task may be viewed as an association finding task, between topics and bloggers. Under this view, it resembles the expert finding task, for which a range of models have been proposed. We adopt two language modeling-based approaches to expert finding, and determine their effectiveness as feed distillation strategies. The two models capture the idea that a human will often search for key blogs by spotting highly relevant posts (the Posting model) or by taking global aspects of the blog into account (the Blogger model). Results show the Blogger model outperforms the Posting model and delivers state-of-the art performance, out-of-the-box.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {753–754},
numpages = {2},
keywords = {people-topic associations, language modeling, feed distillation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390487,
author = {Fetterly, Dennis and Craswell, Nick and Vinay, Vishwa},
title = {Search Effectiveness with a Breadth-First Crawl},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390487},
doi = {10.1145/1390334.1390487},
abstract = {Previous scalability experiments found that early precision improves as collection size increases. However, that was under the assumption that a collection's documents are all sampled with uniform probability from the same population. We contrast this to a large breadth-first web crawl, an important scenario in real-world Web search, where the early documents have quite different characteristics from the later documents.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {755–756},
numpages = {2},
keywords = {search quality, web crawling, corpus quality},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390488,
author = {Guan, Ziyu and Wang, Can and Chen, Chun and Bu, Jiajun and Wang, Junfeng},
title = {Guide Focused Crawler Efficiently and Effectively Using On-Line Topical Importance Estimation},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390488},
doi = {10.1145/1390334.1390488},
abstract = {Focused crawling is a critical technique for topical resource discovery on the Web. We propose a new frontier prioritizing algorithm, namely, the OTIE (On-line Topical Importance Estimation) algorithm, which efficiently and effectively combines link-based and content-based analysis to evaluate the priority of an uncrawled URL in the frontier. We then demonstrate OTIE's advantages over traditional prioritizing algorithms by real crawling experiments.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {757–758},
numpages = {2},
keywords = {focused crawlers, topical crawlers},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390489,
author = {Maekawa, Takuya and Yanagisawa, Yutaka and Sakurai, Yasushi and Kishino, Yasue and Kamei, Koji and Okadome, Takeshi},
title = {Web Page Retrieval in Ubiquitous Sensor Environments},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390489},
doi = {10.1145/1390334.1390489},
abstract = {This paper proposes new concept of query free web search for daily living. We ordinarily benefit from additional information about our daily activities that we are currently engaged in. When washing a coffee maker, for example, we receive the benefit if we obtain such information as 'cleaning a coffee maker with vinegar removes its stain well.' Our proposed method automatically searches for a web page including such information relates to an activity of daily living when the activity is performed. We assume that wireless sensor nodes are attached to daily objects to detect object use; our method makes a query from the names of objects which are used. Then, the method retrieves a web page relates to the activity of daily living by using the query.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {759–760},
numpages = {2},
keywords = {web search, wireless sensor node, daily living},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390490,
author = {Peng, Jie and Macdonald, Craig and Ounis, Iadh},
title = {Automatic Document Prior Feature Selection for Web Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390490},
doi = {10.1145/1390334.1390490},
abstract = {Document prior features, such as Pagerank and URL depth, can improve the retrieval effectiveness of Web Information Retrieval (IR) systems. However, not all queries equally benefit from the application of a document prior feature. This paper aims to investigate whether the retrieval performance can be further enhanced by selecting the best document prior feature on a per-query basis. We present a novel method for selecting the best document prior feature on a per-query basis. We evaluate our technique on the TREC .GOV Web test collection and its associated TREC 2003 Web search tasks. Our experiments demonstrate the effectiveness and robustness of our proposed selection method.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {761–762},
numpages = {2},
keywords = {prior feature selection},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390491,
author = {Kaptein, Rianne and LI, Rongmei and Hiemstra, Djoerd and Kamps, Jaap},
title = {Using Parsimonious Language Models on Web Data},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390491},
doi = {10.1145/1390334.1390491},
abstract = {In this paper we explore the use of parsimonious language models for web retrieval. These models are smaller thus more efficient than the standard language models and are therefore well suited for large-scale web retrieval. We have conducted experiments on four TREC topic sets, and found that the parsimonious language model results in improvement of retrieval effectiveness over the standard language model for all data-sets and measures. In all cases the improvement is significant, and more substantial than in earlier experiments on newspaper/newswire data.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {763–764},
numpages = {2},
keywords = {language models, parsimonious language models, web retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390492,
author = {Nguyen, Doan},
title = {Query Preprocessing: Improving Web Search through a Vietnamese Word Tokenization Approach},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390492},
doi = {10.1145/1390334.1390492},
abstract = {In this poster paper, we propose a novel approach to improve web search relevancy by tokenizing a Vietnamese query text prior submitting it to a search engine. Evaluations demonstrate its effectiveness and practical value.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {765–766},
numpages = {2},
keywords = {Vietnamese word tokenization, search, query preprocessing},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390494,
author = {Liao, Wei-Shing and Chen, Kuan-Ting and Hsu, Winston H.},
title = {AdImage: Video Advertising by Image Matching and Ad Scheduling Optimization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390494},
doi = {10.1145/1390334.1390494},
abstract = {With the prevalence of recording devices and the ease of media sharing, consumers are embracing huge amounts of Internet videos. There arise the needs for effective video advertisement systems following their phenomenal success in text. We propose a novel advertising system, AdImage, which automatically associates relevant ads by matching characteristic images, referred to as adImages (analogous to adWords) here. The proposed image matching method is invariant to certain distortions commonly observed in shared videos. AdImage also avoids the pitfalls of poor tagging qualities in shared videos and provides a brand-new venue to specify ad targets by image objects. Moreover, we formulate the image matching scores and the parameterized bidding information as a nonlinear optimization problem for maximizing the system revenues and user perception.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {767–768},
numpages = {2},
keywords = {web video advertising, image matching, AdImage, content-aware},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390495,
author = {Jiang, Yu-Gang and Ngo, Chong-Wah},
title = {Bag-of-Visual-Words Expansion Using Visual Relatedness for Video Indexing},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390495},
doi = {10.1145/1390334.1390495},
abstract = {Bag-of-visual-words (BoW) has been popular for visual classification in recent years. In this paper, we propose a novel BoW expansion method to alleviate the effect of visual word correlation problem. We achieve this by diffusing the weights of visual words in BoW based on visual word relatedness, which is rigorously defined within a visual ontology. The proposed method is tested in video indexing experiment on TRECVID-2006 video retrieval benchmark, and an improvement of 7% over the traditional BoW is reported.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {769–770},
numpages = {2},
keywords = {bag-of-visual-words, expansion, video indexing, visual relatedness},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390496,
author = {Li, Linlin and Tan, Chew Lim},
title = {A Word Shape Coding Method for Camera-Based Document Images},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390496},
doi = {10.1145/1390334.1390496},
abstract = {This paper reports a word shape coding method to facilitate retrieval of camera-based document images without OCR. Due to perspective distortion, many reported word shape coding methods fail on camera-based images. In this paper, the problem is addressed by approximating the perspective transformation with an affine transformation, and employing an affine invariant, namely length ratio, to represent the connected components. Components in a document image are classified into a few clusters, each of which is assigned with a representative symbol. Retrieval are based on "words" comprising of symbols. The experiment results showed that the proposed method achieved an average retrieval precision of 93.43% and recall of 94.22%.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {771–772},
numpages = {2},
keywords = {document image retrieval, perspective distortion},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390497,
author = {Tsagkias, Manos and Larson, Martha and de Rijke, Maarten},
title = {Term Clouds as Surrogates for User Generated Speech},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390497},
doi = {10.1145/1390334.1390497},
abstract = {User generated spoken audio remains a challenge for Automatic Speech Recognition (ASR) technology and content-based audio surrogates derived from ASR-transcripts must be error robust. An investigation of the use of term clouds as surrogates for podcasts demonstrates that ASR term clouds closely approximate term clouds derived from human-generated transcripts across a range of cloud sizes. A user study confirms the conclusion that ASR-clouds are viable surrogates for depicting the content of podcasts.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {773–774},
numpages = {2},
keywords = {automatic speech recognition, term cloud, user generated spoken content, tag cloud},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390498,
author = {Villa, Robert and Gildea, Nicholas and Jose, Joemon M.},
title = {A Faceted Interface for Multimedia Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390498},
doi = {10.1145/1390334.1390498},
abstract = {With the rapid increase in online video services, video retrieval systems are becoming increasingly important search tools to many users in many different fields. In this poster we present a novel video retrieval interface, which supports the creation of multiple search "facets", to aid users carrying out complex, multi-faceted search tasks. The interface allows multiple searches to be executed and viewed simultaneously, and allows material to be reorganized between the facets. An experiment is presented which compares the faceted interface to a tabbed interface similar to that on modern web browsers, and some preliminary results are given.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {775–776},
numpages = {2},
keywords = {collaboration, awareness, multimedia retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390499,
author = {Xu, Hongtao and Zhou, Xiangdong and Lin, Lan},
title = {WISA: A Novel Web Image Semantic Analysis System},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390499},
doi = {10.1145/1390334.1390499},
abstract = {We present a novel Web Image Semantic Analysis (WISA) system, which explores the problem of adaptively modeling the distributions of the semantic labels of the web image on its surrounding text. To deal with this problem, we employ a new piecewise penalty weighted regression model to learn the weights of the contributions of the different parts of the surrounding text to the semantic labels of images. Experimental results on a real web image data set show that it can improve the performance of web image semantic annotation significantly.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {777–778},
numpages = {2},
keywords = {regression model, image semantic annotation, web multimedia management},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390500,
author = {Okamoto, Masayuki and Kikuchi, Masaaki and Yamasaki, Tomohiro},
title = {One-Button Search Extracts Wider Interests: An Empirical Study with Video Bookmarking Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390500},
doi = {10.1145/1390334.1390500},
abstract = {This poster presents an overview of the characteristics of a one-button information retrieval interface with closed captions from TV watching activities, which is intended to lighten the burden of remembering and entering query terms while watching TV. We investigated this interface with an experimental system named Video Bookmarking Search, which estimates query terms from closed captions with named-entity recognition and sentence labeling techniques. According to an empirical evaluation for 1,138 search queries from 206 bookmarks using seven actual TV shows on city life, travel, health, and cuisine, we found wider queries and search results are acceptable through the query-input-free interface, despite the fact that the number of queries and search results that are directly relevant to the users' original intentions is not high. The main reason is a watching user's interest is wider than what is expressed with query terms.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {779–780},
numpages = {2},
keywords = {acceptability, user satisfaction, closed captions},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390501,
author = {Nurmi, Petteri and Lagerspetz, Eemil and Buntine, Wray and Flor\'{e}en, Patrik and Kukkonen, Joonas},
title = {Product Retrieval for Grocery Stores},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390501},
doi = {10.1145/1390334.1390501},
abstract = {We introduce a grocery retrieval system that maps shopping lists written in natural language into actual products in a grocery store. We have developed the system using nine months of shopping basket data from a large Finnish supermarket. To evaluate the system, we used 70 real shopping lists gathered from customers of the supermarket. Our system achieves over 80% precision for products at rank one, and the precision is around 70% for products at rank 5.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {781–782},
numpages = {2},
keywords = {user evaluation, retrieval models, grocery retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390502,
author = {Hu, Qinmin and Huang, Xiangji},
title = {A Reranking Model for Genomics Aspect Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390502},
doi = {10.1145/1390334.1390502},
abstract = {In this paper, we propose a reranking model to improve the aspect-level performance in the biomedical domain. This model iteratively computes the maximum hidden aspect for every retrieved passage and then reranks these passages from aspect subsets. The experimental results show the improvements of the aspect-level performance up to 27.14% for 2006 Genomics topics and 27.09% for 2007 Genomics topics.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {783–784},
numpages = {2},
keywords = {genomics IR, aspect search, reranking},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390503,
author = {Wang, Shuguang and Hauskrecht, Milos},
title = {Improving Biomedical Document Retrieval Using Domain Knowledge},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390503},
doi = {10.1145/1390334.1390503},
abstract = {Research articles typically introduce new results or findings and relate them to knowledge entities of immediate relevance. However, a large body of context knowledge related to the results is often not explicitly mentioned in the article. To overcome this limitation the state-of-the-art information retrieval approaches rely on the latent semantic analysis in which terms in articles are projected to a lower dimensional latent space and best possible matches in this space are identified. However, this approach may not perform well enough if the number of explicit knowledge entities in the articles is too small compared to the amount of knowledge in the domain. We address the problem by exploiting a domain knowledge layer, a rich network of relations among knowledge entities in the domain extracted from a large corpus of documents. The knowledge layer supplies the context knowledge that lets us relate different knowledge entities and hence improve the information retrieval performance. We develop and study a new framework for i) learning and aggregating the relations in the knowledge layer from the literature corpus; ii) and for exploiting these relations to improve the information-retrieval of relevant documents.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {785–786},
numpages = {2},
keywords = {information retrieval, link analysis},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390504,
author = {Nobata, Chikashi and Cotter, Philip and Okazaki, Naoaki and Rea, Brian and Sasaki, Yutaka and Tsuruoka, Yoshimasa and Tsujii, Jun'ichi and Ananiadou, Sophia},
title = {Kleio: A Knowledge-Enriched Information Retrieval System for Biology},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390504},
doi = {10.1145/1390334.1390504},
abstract = {Kleio is an advanced information retrieval (IR) system developed at the UK National Centre for Text Mining (NaCTeM)1. The system offers textual and metadata searches across MEDLINE and provides enhanced searching functionality by leveraging terminology management technologies.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {787–788},
numpages = {2},
keywords = {named entity recognition, information retrieval, MEDLINE},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390505,
author = {Tang, Xiaoya},
title = {Enhancing Keyword-Based Botanical Information Retrieval with Information Extraction},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390505},
doi = {10.1145/1390334.1390505},
abstract = {Keyword-based retrieval matches search terms and documents via term co-occurrence. Such an approach does not allow matching based on the specific plant characteristic descriptions that are often used in botanical text retrieval. This study applies information extraction techniques to automatically extract plant characteristic information from text and allows users to search using such information in combination with keywords. An evaluation experiment was conducted using actual users. The results indicate that this approach enhances task-based retrieval performance.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {789–790},
numpages = {2},
keywords = {information retrieval, information extraction, botanical text retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390506,
author = {White, Ryen W. and Dumais, Susan and Teevan, Jaime},
title = {How Medical Expertise Influences Web Search Interaction},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390506},
doi = {10.1145/1390334.1390506},
abstract = {Domain expertise can have an important influence on how people search. In this poster we present findings from a log-based study into how medical domain experts search the Web for information related to their expertise, as compared with non-experts. We find differences in sites visited, query vocabulary, and search behavior. The findings have implications for the automatic identification of domain experts from interaction logs, and the use of domain knowledge in applications such as query suggestion or page recommendation to support non-experts.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {791–792},
numpages = {2},
keywords = {domain expertise, web search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390507,
author = {Seki, Kazuhiro and Hattori, Hiroyuki and Uehara, Kuniaki},
title = {Generating Diverse Katakana Variants Based on Phonemic Mapping},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390507},
doi = {10.1145/1390334.1390507},
abstract = {In Japanese, it is quite common for the same word to be written in several different ways. This is especially true for katakana words which are typically used for transliterating foreign languages. This ambiguity becomes critical for automatic processing such as information retrieval (IR). To tackle this problem, we propose a simple but effective approach to generating katakana variants by considering phonemic representation of the original language for a given word. The proposed approach is evaluated through an assessment of the variants it generates. Also, the impact of the generated variants on IR is studied in comparison to an existing approach using katakana rewriting rules.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {793–794},
numpages = {2},
keywords = {information retrieval, katakana variants, transliteration},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390508,
author = {Serdyukov, Pavel and Rode, Henning and Hiemstra, Djoerd},
title = {Exploiting Sequential Dependencies for Expert Finding},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390508},
doi = {10.1145/1390334.1390508},
abstract = {We propose an expert finding method based on assumption of sequential dependence between a candidate expert and the query terms in the scope of a document. We assume that the strength of relation of a candidate to the document's content depends on its position in this document with respect to the positions of the query terms. The experiments on the official Enterprise TREC data demonstrate the advantage of our method over the method based on independence of query terms and persons in a document.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {795–796},
numpages = {2},
keywords = {expert finding, enterprise search, dependence modeling},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390509,
author = {Serdyukov, Pavel and Rode, Henning and Hiemstra, Djoerd},
title = {Modeling Expert Finding as an Absorbing Random Walk},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390509},
doi = {10.1145/1390334.1390509},
abstract = {We introduce a novel approach to expert finding based on multi-step relevance propagation from documents to related candidates. Relevance propagation is modeled with an absorbing random walk. The evaluation on the two official Enterprise TREC data sets demonstrates the advantage of our method over the state-of-the-art method based on one-step propagation.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {797–798},
numpages = {2},
keywords = {expert finding, random walks, enterprise search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390510,
author = {Crain, Steven P. and Huang, Jian and Zha, Hongyuan},
title = {A Scalable Assistant Librarian: Hierarchical Subject Classification of Books},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390510},
doi = {10.1145/1390334.1390510},
abstract = {In this paper, we discuss our work in progress towards a scalable hierarchical classification system for books using the Library of Congress subject hierarchy. We examine the characteristics of this domain which make the problem very challenging, and we look at several appropriate performance measurements. We show that both Hieron and Hierarchical Support Vector Machines perform moderately well.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {799–800},
numpages = {2},
keywords = {support vector machine, hieron, hierarchical classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390511,
author = {Chen, Ing-Xiang and Jaygarl, Hojun and Yang, Cheng-Zen and Wu, Ping-Jung},
title = {Information Retrieval on Bug Locations by Learning Co-Located Bug Report Clusters},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390511},
doi = {10.1145/1390334.1390511},
abstract = {Bug locating usually involves intensive search activities and incurs unpredictable cost of labor and time. An issue of information retrieval on bug locations is particularly addressed to facilitate identifying bugs from software code. In this paper, a novel bug retrieval approach with co-location shrinkage (CS) is proposed. The proposed approach has been implemented in open-source software projects collected from real-world repositories, and consistently improves the retrieval accuracy of a state-of-the-art Support Vector Machine (SVM) model.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {801–802},
numpages = {2},
keywords = {bug retrieval, bug report managing systems, support vector machine, co-location shrinkage},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390512,
author = {Garain, Utpal},
title = {Summarization of Compressed Text Images: An Experience on Indic Script Documents},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390512},
doi = {10.1145/1390334.1390512},
abstract = {Automatic summarization of JBIG2 coded textual images is discussed. Compressed images are partially decompressed to compute relevant features. The feature extraction method is free from using any character recognition module. Summary sentences are ranked. Experiment considers documents in Indic scripts that lack in having any efficient OCR systems. Script independent aspect of the approach is highlighted through use of two most popular Indic scripts. Sentence selection efficiency of about 61% is achieved when judged against man-made summarization. A nonparametric (distribution-free) rank statistic shows a correlation coefficient of 0.33 as a measure of the (minimum) strength of the associations between sentence ranking by machine and human.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {803–804},
numpages = {2},
keywords = {summarization, textual images, JBIG2, performance evaluation, Indic script, compression domain retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390514,
author = {Diaz, Fernando D.},
title = {A Method for Transferring Retrieval Scores between Collections with Non-Overlapping Vocabularies},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390514},
doi = {10.1145/1390334.1390514},
abstract = {We present a method for projecting retrieval scores across two corpora with a shared, parallel corpus.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {805–806},
numpages = {2},
keywords = {regularization, cross-lingual retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390515,
author = {Diaz, Fernando D.},
title = {Improving Relevance Feedback in Language Modeling with Score Regularization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390515},
doi = {10.1145/1390334.1390515},
abstract = {We demonstrate that regularization can improve feedback in a language modeling framework.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {807–808},
numpages = {2},
keywords = {language modeling, relevance feedback, regularization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390516,
author = {Diaz, Fernando D.},
title = {Theoretical Bounds on and Empirical Robustness of Score Regularization to Different Similarity Measures},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390516},
doi = {10.1145/1390334.1390516},
abstract = {We present theoretical bounds and empirical robustness of score regularization given changes in the similarity measure.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {809–810},
numpages = {2},
keywords = {inter-document similarity, regularization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390517,
author = {Arampatzis, Avi and Kamps, Jaap},
title = {A Study of Query Length},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390517},
doi = {10.1145/1390334.1390517},
abstract = {We analyse query length, and fit power-law and Poisson distributions to four different query sets. We provide a practical model for query length, based on the truncation of a Poisson distribution for short queries and a power-law distribution for longer queries, that better fits real query length distributions than earlier proposals.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {811–812},
numpages = {2},
keywords = {Zipf's law, transaction log analysis, power-law, query length},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390518,
author = {McNamee, Paul and Nicholas, Charles and Mayfield, James},
title = {Don't Have a Stemmer? Be Un+concern+ed},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390518},
doi = {10.1145/1390334.1390518},
abstract = {The choice of indexing terms used to represent documents crucially determines how e ective subsequent retrieval will be. IR systems commonly use rule-based stemmers to normalize surface word forms to combat the problem of not finding documents that contain words related to query terms by inflectional or derivational morphology. But such stemmers are not available in all languages. In this paper we explore the effectiveness of unsupervised morphological segmentation as an alternative to stemming using test sets in thirteen European languages. We find that unsupervised segmentation is significantly better than unnormalized words, in several cases by more than 20%. However, rule-based stemming, if available, is better in low complexity languages. We also compare these methods to the use of character n-grams, finding that on average n-grams yield the best performance.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {813–814},
numpages = {2},
keywords = {unsupervised morphological segmentation, stemming, tokenization, character n-grams},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390519,
author = {Meij, Edgar and Trieschnigg, Dolf and de Rijke, Maarten and Kraaij, Wessel},
title = {Parsimonious Concept Modeling},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390519},
doi = {10.1145/1390334.1390519},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {815–816},
numpages = {2},
keywords = {parsimonious models, language models, relevance feedback},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390520,
author = {Meij, Edgar and Weerkamp, Wouter and Balog, Krisztian and de Rijke, Maarten},
title = {Parsimonious Relevance Models},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390520},
doi = {10.1145/1390334.1390520},
abstract = {We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test collections in five different genres. Our parsimonious relevance models (i) improve retrieval effectiveness in terms of MAP on all collections, (ii) significantly outperform their non-parsimonious counterparts on most measures, and (iii) have a precision enhancing effect, unlike other blind relevance feedback methods.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {817–818},
numpages = {2},
keywords = {relevance feedback, language models, parsimonious models},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390521,
author = {Peng, Wei and Li, Tao},
title = {Author-Topic Evolution Analysis Using Three-Way Non-Negative Paratucker},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390521},
doi = {10.1145/1390334.1390521},
abstract = {Analyzing three-way data has attracted a lot of attention recently due to the intrinsic rich structures in real-world datasets. The PARATUCKER model has been proposed to combine the axis capabilities of the Parafac model and the structural generality of the Tucker model. However, no algorithms have been developed for fitting the PARATUCKER model. In this paper, we propose TANPT algorithm to solve the PARATUCKER model. We apply the algorithm for temporal relation co-clustering on author-topic evolution. Experiments on DBLP datasets demonstrate its effectiveness.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {819–820},
numpages = {2},
keywords = {author-topic evolution, tensor, three-way data},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390522,
author = {Na, Seung-Hoon and Kim, Jungi and Kang, In-Su and Lee, Jong-Hyeok},
title = {Exploiting Proximity Feature in Bigram Language Model for Information Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390522},
doi = {

license_type: permissive