--- title: "Uniformisation de corpus anglais annotés en sens (Unification of" description: "English word sense disambiguation (WSD) has roughly fifteen sense-annotated corpora. 10 references." authors: ["Loïc Vial", "Benjamin Lecouteux", "Didier Schwab"] year: 2017 venue: "Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations" source: "https://aclanthology.org/2017.jeptalnrecital-demo.9/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/vial-2017-uniformisation-de-corpus-anglais-annotes/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/vial-2017-uniformisation-de-corpus-anglais-annotes/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2017](https://md.research.elamur.ai/free/en/papers/2017/page/6/) / Uniformisation de corpus anglais annotés en sens (Unification of sense annotated English corpora for word sense disambiguation) # Uniformisation de corpus anglais annotés en sens (Unification of sense annotated English corpora for word sense disambiguation) **Authors:** [Loïc Vial](https://md.research.elamur.ai/free/en/authors/loic-vial/), [Benjamin Lecouteux](https://md.research.elamur.ai/free/en/authors/benjamin-lecouteux/) and [Didier Schwab](https://md.research.elamur.ai/free/en/authors/didier-schwab/) · **Year:** 2017 · **Venue:** [Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations](https://md.research.elamur.ai/free/en/venues/jeptalnrecital/) ([JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing) 2017](https://md.research.elamur.ai/free/en/venues/jeptalnrecital/2017/)) · **Publisher:** [ATALA](https://md.research.elamur.ai/free/en/publishers/atala/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2017.jeptalnrecital-demo.9/) · [PDF](https://aclanthology.org/2017.jeptalnrecital-demo.9.pdf) · [OpenAlex](https://openalex.org/W2773426197) **Citations:** 1 citations (OpenAlex, 2026-10-14) ## Summary English word sense disambiguation (WSD) has roughly fifteen sense-annotated corpora. They come in different file formats and use different releases of Princeton WordNet, so combining them takes real effort. Habit also splits them unevenly: systems are scored only on corpora built for evaluation, with no scientific reason to exclude the others. The authors gather twelve corpora into one simple XML format. They convert every sense annotation to WordNet 3.0 using mapping tables built with the method of Daudé et al. (2000). The collection includes training-oriented corpora such as SemCor, DSO, OMSTI, MASC, OntoNotes and the WordNet gloss corpus, plus the six SemEval/SensEval evaluation sets. All corpora are treated as equals rather than split into train and test. The contribution is a resource, with no experimental scores. It includes Java code to read and write the format, the conversion scripts, and the corpora in original and unified form where licenses allow. This makes larger-scale WSD evaluation possible, for example by rotating each corpus in turn as the test set. ## Key points - The resource covers 12 corpora, compared with 7 in the similar effort by Raganato et al. (2017). That work also keeps a fixed split into training and evaluation sets. - Corpora and their original WordNet versions: - SemCor: WordNet 1.6 - DSO: WordNet 1.5 - WordNet gloss corpus: WordNet 3.0 - OMSTI, MASC and OntoNotes: WordNet 3.0 - Six SemEval/SensEval English tasks - All senses are mapped to Princeton WordNet 3.0 through version-to-version mapping tables based on Daudé et al. (2000). - The XML format has five nested elements: corpus, document, paragraph, sentence and word. - Any element may carry arbitrary attributes. - Only `surface_form` on a word is required. - Standard attributes are `pos`, `lemma`, and a version-tagged sense key such as `wn30_key`. - Code, original data and converted data are on GitHub (getalp/WSD-TALN2017-Corpus-Vialetal). Copyrighted corpora can be rebuilt from their originals using the shipped scripts. ## Interesting ideas - Treating every annotated corpus as both potential training and test material allows a leave-one-corpus-out rotation, which tests generalization across domains instead of on one fixed benchmark. - A minimal schema with one mandatory field and open optional attributes keeps a format easy to read while still preserving every detail the source corpora provide. - When licenses block redistribution, shipping the conversion code lets users rebuild the unified data from originals they obtain themselves. - Mapping old sense labels to a single current inventory through structural mapping tables makes legacy annotations usable together. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): Rotate our treebanks and silver sets so each in turn is held out, revealing parser or frame-extractor weaknesses that only appear on unseen sources. - Encoding the inventory version inside the attribute name (e.g. `wn30_key`) lets annotations from several lexicon releases sit side by side without ambiguity. — (world): Tag compiled WordNet/FrameNet/Wikidata links with their source version, so ontology upgrades can be migrated and audited rather than silently overwritten. - **I2** (tree): An internal interchange format for converted UD dialects could require only token form and keep dialect-specific features as optional attributes, so no source information is lost. - **I3** (tree): Distribute treebank-conversion scripts instead of converted restricted treebanks, keeping the pipeline reproducible and legally clean. - **I4** (world): Remap older-WordNet sense annotations onto our WordNet 3.0-based ontology, enlarging the evidence behind each concept and verb class. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Université Grenoble Alpes](https://md.research.elamur.ai/free/en/universities/universite-grenoble-alpes/) — University, Saint-Martin-d'Hères, France · 24 works · 8,259 citations - [Laboratoire d'Informatique de Grenoble](https://md.research.elamur.ai/free/en/labs/laboratoire-d-informatique-de-grenoble/) — Research institute, Grenoble, France · 24 works · 689 citations 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works 🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works · [🔀 Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/) · 87 works ## Cite Cite as: Loïc Vial, Benjamin Lecouteux and Didier Schwab. 2017. Uniformisation de corpus anglais annotés en sens (Unification of sense annotated English corpora for word sense disambiguation). Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{vial2017uniformisation, title = {{Uniformisation de corpus anglais annotés en sens (Unification of sense annotated English corpora for word sense disambiguation)}}, author = {Loïc Vial, Benjamin Lecouteux and Didier Schwab}, booktitle = {Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 3 - Démonstrations}, year = {2017}, url = {https://aclanthology.org/2017.jeptalnrecital-demo.9/} } ``` - [← previous The verse of Gen 1, 1: a subordinate or principal clause? A contribution to the discussion on the syntax of the first sentence of the Hebrew Bible](https://md.research.elamur.ai/free/en/papers/dzikiewicz-2017-verse-gen-1-1-subordinate/) - [next → Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task](https://md.research.elamur.ai/free/en/papers/more-2017-universal-joint-morph-syntactic-processing/) - [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/6/) ## References 10 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. D AUDÉ J., P ADRÓ L. & R IGAU G. (2000). Mapping wordnets using structural information. In Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, ACL ’00, p. 504–511, Stroudsburg, PA, USA : Association for Computational Linguistics. 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[Masc : the manually annotated sub-corpus of american english](https://md.research.elamur.ai/free/en/papers/ide-2008-masc-manually-annotated-sub-corpus/). In B. M. J. M. J. O. S. P. D. T. N ICOLETTA C ALZOLARI (C ONFERENCE C HAIR ), K HALID C HOUKRI , Ed., Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco : European Language Resources Association (ELRA). http ://www.lrec-conf.org/proceedings/lrec2008/. 6. N AVIGLI R., L ITKOWSKI K. C. & H ARGRAVES O. (2007). [Semeval-2007 task 07 : Coarse-grained english all-words task](https://md.research.elamur.ai/free/en/papers/navigli-2007-semeval-2007-task-07-coarse/). In SemEval-2007, p. 30–35, Prague, Czech Republic. 7. N AVIGLI R. & P ONZETTO S. P. (2010). [Babelnet : Building a very large multilingual semantic network](https://md.research.elamur.ai/free/en/papers/navigli-2010-babelnet-building-very-large-multilingual/). In Proceedings of the 48th annual meeting of the association for computational linguistics, p. 216–225 : Association for Computational Linguistics. 8. N G H. T. & L EE H. B. (1996). [Integrating multiple knowledge sources to disambiguate word sense : an exemplar-based approach](https://md.research.elamur.ai/free/en/papers/ng-1996-integrating-multiple-knowledge-sources-disambiguate/). In Proceedings of the 34th annual meeting on Association for Computational Linguistics, ACL ’96, p. 40–47, Stroudsburg, PA, USA : Association for Computational Linguistics. 9. R AGANATO A., C AMACHO -C OLLADOS J. & N AVIGLI R. (2017). [Word sense disambiguation : A unified evaluation framework and empirical comparison](https://md.research.elamur.ai/free/en/papers/raganato-2017-word-sense-disambiguation-unified-evaluation/). In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics : Volume 1, Long Papers, p. 99–110, Valencia, Spain : Association for Computational Linguistics. 10. T AGHIPOUR K. & N G H. T. (2015). One million sense-tagged instances for word sense disambiguation and induction. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning, p. 338–344, Beijing, China : Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=T+AGHIPOUR+K%2E+%26+N+G+H%2E+T%2E+%282015%29%2E+One+million+sense%2Dtagged+instances+for+word+sense+disambiguation+and+induction%2E+In+Proceedings+of+the+Nineteenth+Conference+on+Computational+Natural+Language+Learning) *Updated 2 November 2026*