Add coarse-grained and fine-grained WiC datasets (WiC is Not WSD)

README.md

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+---
+language:
+- en
+pretty_name: "WiC is Not WSD: Coarse-Grained and Fine-Grained WiC"
+task_categories:
+- text-classification
+task_ids:
+- semantic-similarity-classification
+tags:
+- word-in-context
+- wic
+- word-sense-disambiguation
+- lexical-semantics
+- lexical-ambiguity
+- wordnet
+- coarsewsd-20
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: coarse_grained
+  data_files:
+  - split: test
+    path: data/coarse_grained/test.jsonl
+- config_name: fine_grained
+  data_files:
+  - split: test
+    path: data/fine_grained/test.jsonl
+---
+
+# WiC is Not WSD: Coarse-Grained and Fine-Grained Word-in-Context Datasets
+
+Two Word-in-Context (WiC) evaluation datasets from the paper
+**"WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution"**
+(Yi Zhou, Kiamehr Rezaee, Danushka Bollegala, Mohammad Taher Pilehvar, Jose Camacho-Collados).
+
+Given two sentences that both contain the same target word, the task is to decide whether the
+word is used with the **same meaning** in both sentences (`True`) or with **different meanings** (`False`).
+
+The two datasets differ in the granularity of the sense distinctions they encode:
+
+| Config | Instances | Target words | Distinct senses | Unique sentences | True | False |
+|---|---|---|---|---|---|---|
+| `coarse_grained` | 2,798 | 20 | 53 | 5,594 | 1,399 | 1,399 |
+| `fine_grained` | 1,185 | 924 | 1,655 | 2,266 | 594 | 591 |
+
+Both datasets are intended for **evaluation only** and are provided as a single `test` split.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+coarse = load_dataset("JodieChou/wic-llms", "coarse_grained", split="test")
+fine   = load_dataset("JodieChou/wic-llms", "fine_grained", split="test")
+```
+
+## Configs
+
+### `coarse_grained`
+
+Constructed automatically from **CoarseWSD-20** (Loureiro et al., 2021), a coarse-grained WSD dataset
+covering 20 ambiguous nouns. Sentences containing the same target word are paired and the WiC label
+is derived by comparing their coarse-grained sense labels: same sense label gives `True`, otherwise `False`.
+The label distribution is exactly balanced.
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | int | Row index (0-based). |
+| `word` | string | Target word (one of 20 nouns). |
+| `pair_id` | string | Instance identifier carried over from the CoarseWSD-20 conversion (format `x-y`). |
+| `sentence1` | string | First context. Tokenised, lower-cased text from CoarseWSD-20 (Wikipedia). |
+| `sentence2` | string | Second context. |
+| `label` | string | `"True"` if the target word has the same coarse sense in both sentences, else `"False"`. |
+
+Per-word statistics (from the paper, Table 8):
+
+| Word | Senses | Pairs | True | False |
+|---|---|---|---|---|
+| apple | 2 | 424 | 212 | 212 |
+| arm | 2 | 70 | 35 | 35 |
+| bank | 2 | 46 | 23 | 23 |
+| bass | 3 | 508 | 254 | 254 |
+| bow | 3 | 106 | 53 | 53 |
+| chair | 2 | 40 | 20 | 20 |
+| club | 3 | 96 | 48 | 48 |
+| crane | 2 | 78 | 39 | 39 |
+| deck | 2 | 14 | 7 | 7 |
+| digit | 2 | 10 | 5 | 5 |
+| hood | 3 | 38 | 19 | 19 |
+| java | 2 | 824 | 412 | 412 |
+| mole | 5 | 70 | 35 | 35 |
+| pitcher | 2 | 26 | 13 | 13 |
+| pound | 2 | 20 | 10 | 10 |
+| seal | 4 | 108 | 54 | 54 |
+| spring | 3 | 182 | 91 | 91 |
+| square | 4 | 82 | 41 | 41 |
+| trunk | 3 | 38 | 19 | 19 |
+| yard | 2 | 18 | 9 | 9 |
+| **Total** | **53** | **2,798** | **1,399** | **1,399** |
+
+Example:
+
+```json
+{"word": "apple", "pair_id": "6-18",
+ "sentence1": "the local economy includes forestry , apple orchard and sheep and dairy farming .",
+ "sentence2": "symbol is one of the four standard fonts available on all postscript - based printers , starting with apple 's original laserwriter ( 1985 ) .",
+ "label": "False"}
+```
+
+### `fine_grained`
+
+A newly constructed WiC-style dataset that follows the construction of the original WiC benchmark
+(Pilehvar and Camacho-Collados, 2019) but is built from **WordNet 3.1** noun and verb entries, with
+modifications intended to limit the effect of memorisation by language models. Each target word is
+paired with its WordNet gloss, and sentence pairs are built from the usage examples of the senses.
+Unlike standard WiC, each instance also carries the **sense definition** of the target word in each
+sentence, so the dataset can be used both with and without explicit sense information.
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | int | Row index (0-based). |
+| `word` | string | Target word (noun or verb lemma). |
+| `sentence1` | string | First context (WordNet usage example). |
+| `sentence2` | string | Second context (WordNet usage example). |
+| `definition1` | string | WordNet definition of the target word's sense in `sentence1`. |
+| `definition2` | string | WordNet definition of the target word's sense in `sentence2`. |
+| `label` | string | `"True"` if `definition1` and `definition2` are the same sense, else `"False"`. |
+
+Example:
+
+```json
+{"word": "absence",
+ "sentence1": "he was surprised by the absence of any explanation",
+ "sentence2": "he visited during my absence",
+ "definition1": "the state of being absent",
+ "definition2": "the time interval during which something or somebody is away",
+ "label": "False"}
+```
+
+## Files
+
+```
+data/coarse_grained/test.jsonl   # 2,798 rows
+data/fine_grained/test.jsonl     # 1,185 rows
+raw/coarse_grained_WiC.tsv       # original release file, no header: word, pair_id, sentence1, sentence2, label
+raw/fine_grained_WiC.tsv         # original release file, with header; labels are T / F
+```
+
+The JSONL files are the recommended entry point. They add a header, an `id` column, and normalise
+labels in both configs to the strings `"True"` / `"False"` (the raw fine-grained file uses `T` / `F`).
+The raw TSV files are the original release files and are otherwise unchanged.
+
+## Notes
+
+- Sentences in `coarse_grained` are lower-cased and tokenised (punctuation separated by spaces), as in CoarseWSD-20.
+- `coarse_grained` contains one exact duplicate pair (`bank`, `pair_id` `12-5`). It is kept so that the
+  counts match the paper.
+- These are evaluation sets. There is no training split.
+
+## Source datasets
+
+- **CoarseWSD-20**: Daniel Loureiro, Kiamehr Rezaee, Mohammad Taher Pilehvar, and Jose Camacho-Collados. 2021.
+  *Analysis and Evaluation of Language Models for Word Sense Disambiguation.* Computational Linguistics, 47(2):387–443.
+- **WordNet 3.1**: George A. Miller. 1995. *WordNet: A Lexical Database for English.* Communications of the ACM;
+  Christiane Fellbaum (ed.). 1998. *WordNet: An Electronic Lexical Database.* MIT Press.
+- **WiC** (construction procedure): Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019.
+  *WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.* NAACL-HLT 2019.
+
+## Citation
+
+If you use these datasets, please cite the paper:
+
+```bibtex
+@article{zhou2026wicnotwsd,
+  title   = {WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution},
+  author  = {Zhou, Yi and Rezaee, Kiamehr and Bollegala, Danushka and Pilehvar, Mohammad Taher and Camacho-Collados, Jose},
+  year    = {2026}
+}
+```
+
+Please also cite CoarseWSD-20 when using the `coarse_grained` config, and WordNet when using the `fine_grained` config.