Datasets:

TigreGotico
/

search-term-extraction

license: apache-2.0
task_categories:
  - token-classification
language:
  - ca
  - da
  - de
  - en
  - es
  - eu
  - fr
  - gl
  - it
  - nl
  - pt
tags:
  - keyword-extraction
  - search-term-extraction
  - query-understanding
  - voice-assistant
  - topic-extraction
  - ovos
pretty_name: Multilingual Search-Term Extraction
size_categories:
  - 10K<n<100K
configs:
  - config_name: ca
    data_files:
      - split: train
        path: ca/train-*
      - split: test
        path: ca/test-*
  - config_name: da
    data_files:
      - split: train
        path: da/train-*
      - split: test
        path: da/test-*
  - config_name: de
    data_files:
      - split: train
        path: de/train-*
      - split: test
        path: de/test-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
      - split: test
        path: en/test-*
  - config_name: es
    data_files:
      - split: train
        path: es/train-*
      - split: test
        path: es/test-*
  - config_name: eu
    data_files:
      - split: train
        path: eu/train-*
      - split: test
        path: eu/test-*
  - config_name: fr
    data_files:
      - split: train
        path: fr/train-*
      - split: test
        path: fr/test-*
  - config_name: gl
    data_files:
      - split: train
        path: gl/train-*
      - split: test
        path: gl/test-*
  - config_name: it
    data_files:
      - split: train
        path: it/train-*
      - split: test
        path: it/test-*
  - config_name: nl
    data_files:
      - split: train
        path: nl/train-*
      - split: test
        path: nl/test-*
  - config_name: pt
    data_files:
      - split: train
        path: pt/train-*
      - split: test
        path: pt/test-*
dataset_info:
  - config_name: ca
    features:
      - name: lang
        dtype: string
      - name: tokens
        list: string
      - name: tags
        list:
          class_label:
            names:
              '0': O
              '1': B-KW
              '2': I-KW
      - name: text
        dtype: string
      - name: keyword
        dtype: string
      - name: source
        dtype: string
    splits:
      - name: train
        num_bytes: 1197430
        num_examples: 5641
      - name: test
        num_bytes: 215786
        num_examples: 1500
    download_size: 1062253
    dataset_size: 1413216
  - config_name: da
    features:
      - name: lang
        dtype: string
      - name: tokens
        list: string
      - name: tags
        list:
          class_label:
            names:
              '0': O
              '1': B-KW
              '2': I-KW
      - name: text
        dtype: string
      - name: keyword
        dtype: string
      - name: source
        dtype: string
    splits:
      - name: train
        num_bytes: 930479
        num_examples: 4893
      - name: test
        num_bytes: 201595
        num_examples: 1500
    download_size: 850252
    dataset_size: 1132074
  - config_name: de
    features:
      - name: lang
        dtype: string
      - name: tokens
        list: string
      - name: tags
        list:
          class_label:
            names:
              '0': O
              '1': B-KW
              '2': I-KW
      - name: text
        dtype: string
      - name: keyword
        dtype: string
      - name: source
        dtype: string
    splits:
      - name: train
        num_bytes: 1005777
        num_examples: 4902
      - name: test
        num_bytes: 215451
        num_examples: 1500
    download_size: 921793
    dataset_size: 1221228

Multilingual Search-Term Extraction

Token-level labels marking, in a voice-assistant query, the search term — the minimal topic string you would hand to a knowledge base or search engine. Given "what is the speed of light?" the target is "speed of light"; given "set volume to fifty" the target is nothing (there is no topic to look up).

The task is not document keyphrase extraction and not full intent/slot NLU. It answers one question: what do I search for? — the input the OVOS common-query / DuckDuckGo / Wikipedia skills send downstream.

Task & label scheme

Sequence labeling with three tags per token:

tag	meaning
`O`	not part of the search term
`B-KW`	first token of a search-term span
`I-KW`	continuation of a search-term span

Contiguous B-KW/I-KW tokens form the search term; an utterance with no topic (smart-home, timers, volume…) is all O. These negatives are kept on purpose — the extractor runs behind an intent gate but should still not hallucinate a topic where there is none.

Schema

Each row: lang, tokens (list[str], quebra_frases tokenizer), tags (ClassLabel sequence), text, keyword (the target string), source.

Configs & splits

One config per language (ca da de en es eu fr gl it nl pt), each with:

train — ~50k rows total (templated + synthetic; see Provenance).
test — a curated gold split (~16k rows total) from human-authored eval sets.

from datasets import load_dataset
ds = load_dataset("TigreGotico/search-term-extraction", "en")  # train + test

Provenance

Derived from permissively licensed OpenVoiceOS resources and one local-LLM step. The source field on every row records where it came from:

source	what	label quality
`slot_filling`	OVOS locale `{query}` templates (ovos-localize)	deterministic (slot span)
`intents_eval`	intents-for-eval templates (Apache-2.0)	deterministic (content slots)
`massive`	massive-templates, the MASSIVE corpus (Apache-2.0)	deterministic (content slots)
`music`	music_queries_templates (MIT)	deterministic (slot span)
`common_query`	real questions from ovos-common-query-intents	silver — span labelled by a local Gemma model, validated as a verbatim substring
`generated`	questions invented by a local Gemma model	silver — synthetic

The test (gold) split is the -test configs of intents-for-eval and MASSIVE (human-authored utterances with gold slot annotations).

How this dataset was generated

The whole dataset is produced by build_dataset.py in the crf_query_xtract repo (--dry-run-able; deterministic given a seed except for the LLM steps). One span-labelling routine is shared by every source: tokenise with quebra_frases, locate the target value as a token subsequence, and tag that span B-KW/ I-KW — labels therefore always align to the published tokens.

Template sources — deterministic, no LLM. slot_filling, intents_eval, massive and music come from OVOS / MASSIVE {slot} templates. Slots are filled from each template's own example values; the content slot (the search term) is labelled, other slots are filled but left O, and (a|b) alternations are expanded with ovos-spec-tools. Templates whose only slots are constrained (time, volume…) become all-O negatives.
common_query — local-LLM labelling. Real questions from ovos-common-query-intents carry no markup, so a locally-hosted Gemma model (ggml-org/gemma-4-26B-A4B-it, run on the maintainer's own hardware) is asked for the search-term substring; a result is kept only if it is a verbatim substring of the question (otherwise dropped). Treat these as silver.
generated — local-LLM synthesis. The same Gemma model invents extra natural questions and their search term, kept under the same substring check. A small synthetic top-up, mainly for thin languages.
Gold (test) split. Taken verbatim from the human-authored -test configs of intents-for-eval and MASSIVE; the search term is the content-slot value(s) from those datasets' own gold annotations. No LLM labelling.

Transparency on AI involvement

The construction pipeline, the labelling heuristics, and this card were written by Anthropic's Claude operating as an autonomous coding agent. Claude wrote code and documentation — it did not author or label any row of data.
All in-dataset LLM labelling and synthesis (steps 2–3) were done by a local open-weights Gemma model, not by Claude. Roughly 1.5% of train rows are LLM-touched (common_query + generated); the rest are template-derived.
The gold split contains no model-generated labels.

Quality, scope & limitations

Gold vs silver. The test split is curated; in train, common_query and generated are LLM-labelled and the templated sources use a content-slot heuristic (a fixed all-list of entity slot names). Treat train as silver.
Synthetic distribution. Most train rows are templates filled with entities; the real-query distribution differs. common_query and the gold split are the most natural.
What counts as the term follows the source slot boundaries, so leading articles can be included ("the speed of light"). Multi-entity utterances concatenate spans in surface order.
Coverage is uneven: eu and gl are thin (no MASSIVE coverage).

Intended use

Train or evaluate a topic/search-term extractor that sits between intent classification and a search/KB backend. Works for sequence-labeling models (CRF, token classifiers) or as supervision for an LLM. The reference model trained on it is crf_query_xtract.

License & attribution

Apache-2.0. Built from OpenVoiceOS datasets (Apache-2.0 / MIT) and the MASSIVE corpus (Apache-2.0); please credit those upstreams alongside this dataset.