lexEN

A conservative, human-adjudicated correction layer over the standard English all-words word sense disambiguation benchmark.

A panel of models flagged 363 contested items in Maru et al. 2022's ALL_NEW; three professional lexicographers adjudicated those items independently, changing 211 gold labels and removing 56. The other 4,554 items carry their source labels unchanged — they were never reviewed, and lexEN makes no claim about them.

The complete review record is published alongside the data.

Key facts

Task	Word sense disambiguation, all-words, fine-grained
Modality	Text
Language	English
Domain	News, fiction and encyclopaedic prose (Senseval / SemEval source corpora)
Examples	4,861 polysemous items, across 948 sentences and 23 documents
Splits	test only — this is an evaluation set, not training data
Sense inventory	WordNet 3.0
Annotation	363 model-flagged items adjudicated by three professional lexicographers; 4,554 items inherited unreviewed
Agreement	Fleiss κ 0.537 fine / 0.740 coarse, over the 363 reviewed items
Licence	CC BY-NC 4.0 — research and non-commercial evaluation only
Version	lexen-v1
Leaderboard	https://sense-bench.com
Contact	https://github.com/GliteTech/lexen/issues

Quick start

from datasets import load_dataset

items = load_dataset("GliteTech/lexen", split="test")
print(items[0])

# The per-item lexicographer record for the 363 reviewed items
reviews = load_dataset("GliteTech/lexen", "reviews", split="test")

Pin a revision for reproducibility:

items = load_dataset("GliteTech/lexen", split="test", revision="lexen-v1")

No authentication required. ~13 MB total.

Dataset summary

lexEN is a conservative, fully traced correction layer over the standard English all-words WSD evaluation framework — not a new corpus, and not a re-annotation of it. Only 7.4% of items (363 of 4,917) were seen by a human reviewer; the rest are inherited unchanged.

It starts from Raganato et al. 2017, as corrected by Maru et al. 2022, and adds one further round of manual review. A panel of models flagged 363 items where automatic predictions disagreed with the source gold label. Three professional lexicographers then reviewed those items independently — blind to the source label, to which system had flagged the item, and to one another's answers. A two-of-three majority rule, fixed before adjudication, decided each case.

The rule never invents a label; it only ratifies or discards.

Outcome	Items
Retained, unanimous	124
Retained, two-of-three	183
Removed, two or more cannot-answer	27
Removed, no sense consensus	29
Gold labels changed vs source	211

The release is those 307 retained items plus 4,554 unreviewed items carrying their source label unchanged, for 4,861 in total.

Inter-annotator agreement

Computed over the 363 reviewed items from the three lexicographers' raw choices — not over the released set, most of which no reviewer saw. This is the number most likely to be useful to other researchers.

Granularity	Three-way agreement	Fleiss κ
WordNet fine-grained	35.5%	0.537
Coarse sense grouping	63.1%	0.740

Three professional lexicographers, one shared brief, the same items — and only moderate agreement at fine granularity. 42.7% of the items they disagreed on become unanimous once over-specified sense distinctions are collapsed.

A share of what is normally reported as WSD system error is disagreement about the sense inventory rather than about the word. The per-item choices are in the reviews config, so this is auditable rather than asserted.

Dataset structure

Example instance

{
  "item_id": "senseval2.d000.s000.t000",
  "lemma": "art",
  "pos": "NOUN",
  "target_text": "art",
  "sentences": [["The", "art", "of", "change-ringing", "is", "peculiar", "to", "the", "English", "."]],
  "sentence_index": 0,
  "sentence_id": "senseval2.d000.s000",
  "document_id": "senseval2.d000",
  "target_token_index": 1,
  "gold_sense_keys": ["art%1:09:00::"],
  "metadata": {"label_set": "lexen_gold", "dataset_canary": "lexen-canary-v1-..."}
}

Data fields

• item_id — stable identifier, unique within this release. Inherited from the source framework, so it is comparable across Raganato, Maru and lexEN.
• lemma — the lemma of the target word.
• pos — part of speech: NOUN, VERB, ADJ or ADV.
• target_text — the surface form of the target token as it appears in the sentence.
• sentences — a context window as a list of token lists. Tokens are pre-tokenised, not raw text.
• sentence_index — zero-based index into sentences identifying which one contains the target.
• sentence_id, document_id — provenance within the source corpus.
• target_token_index — zero-based index of the target token within its sentence.
• gold_sense_keys — list of correct WordNet 3.0 sense keys. Usually one; a small number of items carry more where the reviewers judged the context genuinely ambiguous.
• metadata — label set, adjudication decision, and the contamination canary.

Candidate senses are not stored per item: they are derived from WordNet 3.0 for the given lemma and pos, so the candidate set is whatever the installed WordNet returns.

The reviews config

One record per reviewed item (363), carrying each reviewer's choice, any typed cannot-answer flag, free-text rationale, and the adjudicated consensus.

Splits

A single test split. There is deliberately no train split — lexEN is an evaluation set, and its source corpora are the standard held-out WSD test sets. Systems are conventionally trained on SemCor, which is disjoint from these items.

Additional formats

Path	What it is
data/items.jsonl	The 4,861 items. Default config.
data/reviews.jsonl	Per-item reviewer choices for the 363 reviewed items.
raganato/lexen-v1.data.xml	Standard Raganato-format XML, for existing WSD tooling.
raganato/lexen-v1.gold.key.txt	Standard gold key file.
raganato/lexen-v1.removed.json	The 56 removed items, each with its removal reason.
DATASHEET.md	Datasheet for the dataset.

The Raganato-format files let existing WSD evaluation scripts run against lexEN unchanged.

Evaluation

data/items.jsonl here is byte-identical to the file the SenseBench leaderboard scores against — SHA-256 5fd4382b93f19087a1e31f6dd7d1db17c1eb17ff80fcbe1d3fdd55c0c3ecefe8 — so results published there apply to this copy without qualification.

pip install sensebench
sensebench run --model <model> --prompt p001 --github-handle <you>
sensebench verify runs/<run-id> --dataset lexen-v1 --prompt p001

The metric is accuracy over items: a prediction is correct when the predicted sense key is in gold_sense_keys. The leaderboard additionally reports bootstrap confidence intervals, rank ranges and paired McNemar tests, and scores classic supervised systems (MFS, BEM, ESCHER, ConSeC) on identical items for reference.

Accuracy on this dataset is meaningful only alongside its sense granularity: the same run can score around ten points differently under fine and coarse inventories. State the label scheme with any number you report.

Contamination

Every item carries a canary string in metadata.dataset_canary:

lexen-canary-v1-U4f9Yzq6Rk2nQ8wL7cP3tB5aHxM1sD0e

If a model reproduces that string, it was trained on this file. Please do not strip it when redistributing, and please exclude lexEN from pretraining corpora.

The source corpora predate lexEN and are long public, so contamination of the underlying text must be assumed. The 211 corrected labels, however, did not exist before this release, which makes them a usable probe: a model echoing memorised gold would score worse on the changed items than on the unchanged ones.

Intended uses

• Evaluating word sense disambiguation in language models and supervised systems
• Studying the granularity of the WordNet sense inventory and its effect on measured accuracy
• Research on annotation quality and inter-annotator disagreement in lexical semantics
• Reproducing results published on the SenseBench leaderboard

Out-of-scope uses

• Training. This is an evaluation set; training on it invalidates it for everyone.
• Commercial use. The licence does not permit it — see below.
• Claiming corpus-wide error rates for the source benchmarks (see Limitations).
• Comparing scores across different sense granularities or gold-label schemes as if commensurable.
• Treating the coarse-grained sense mapping as an independent standard — it was developed by the same authors.

Limitations

• The reviewed subset is model-selected, not random. Every item a lexicographer saw was flagged for disagreeing with the source label. No claim is made about a corpus-wide error rate: the 211 changed labels are a property of the contested items examined, not an extrapolation to the 4,554 that were not reviewed.
• Fine WordNet granularity bounds what any of these numbers mean. Expert agreement is κ 0.537. Scores near the top of the leaderboard sit inside the band where lexicographers themselves disagree.
• Coarse results depend on a sense map we developed, which ships with SenseBench rather than with this dataset. It is released in full, alongside a third-party coarse inventory (CSI), so coarse numbers can be reproduced or replaced.
• English only, and only the written registers of the Senseval and SemEval source corpora — news, fiction and encyclopaedic prose. Nothing here supports claims about conversational speech, learner English or social-media varieties.
• Small. 4,861 items over 948 sentences. Differences of a few tenths of a point are unlikely to be meaningful; use the paired tests.
• The 29 items removed for three-way disagreement are documented rather than silently dropped, and are worth reading before treating any WSD gold standard as definitive.

Personal and sensitive information

lexEN contains no personal or sensitive information beyond what is present in the long-published Senseval and SemEval source corpora, which are edited news, fiction and encyclopaedic prose. No personal data was collected for this release. Reviewer identities are published with their consent as part of the provenance record; no other annotator information is included.

Licensing

Three levels, which are not the same:

1. Dataset artifacts — CC BY-NC 4.0. Inherited from Maru et al. 2022; released for research use and non-commercial evaluation only. This is not ours to relicense.
2. Upstream source content retains its own terms. The Raganato 2017 framework and the Senseval / SemEval corpora carry their original conditions.
3. Build, verification and evaluation software — Apache-2.0, in the lexEN and SenseBench repositories.

Where a form accepts one licence identifier for the dataset, the answer is cc-by-nc-4.0.

Versioning

Current version: lexen-v1.

Releases are immutable. Dataset artifacts are pinned by SHA-256 in sources/manifest.json and data/lexen-v1/dataset.json in the source repository, and verified in CI. A future lexen-v2 will be a separate release rather than an edit to this one, so published scores remain comparable to the version they were measured on.

Citation

@misc{lexen2026,
  title  = {lexEN: A Lexicographer-Reviewed English Word Sense Disambiguation Evaluation Set},
  author = {Philippov, Vassili and {Glite Tech Ltd}},
  year   = {2026},
  url    = {https://github.com/GliteTech/lexen}
}

Please also cite the work this builds on:

@inproceedings{maru-etal-2022-nibbling,
  title     = {Nibbling at the Hard Core of Word Sense Disambiguation},
  author    = {Maru, Marco and Conia, Simone and Bevilacqua, Michele and Navigli, Roberto},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
  year      = {2022}
}

@inproceedings{raganato-etal-2017-word,
  title     = {Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison},
  author    = {Raganato, Alessandro and Camacho-Collados, Jose and Navigli, Roberto},
  booktitle = {Proceedings of EACL},
  year      = {2017}
}

Links

• Source repository, with the full review record: https://github.com/GliteTech/lexen
• Leaderboard: https://sense-bench.com
• Evaluation harness: https://github.com/GliteTech/sensebench — pip install sensebench