---
title: "An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic — Basile 2014"
description: "They then replace literal word counting with similarity in a distributional space. 27 references, cited by 40 papers here."
authors: ["Pierpaolo Basile", "Annalina Caputo", "Giovanni Semeraro"]
year: 2014
venue: "Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers"
source: "https://aclanthology.org/C14-1151/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/basile-2014-enhanced-lesk-word-sense-disambiguation/"
---
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# An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model

**Authors:** [Pierpaolo Basile](https://md.research.elamur.ai/free/en/authors/pierpaolo-basile/), Annalina Caputo and [Giovanni Semeraro](https://md.research.elamur.ai/free/en/authors/giovanni-semeraro/) · **Year:** 2014 · **Venue:** [Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers](https://md.research.elamur.ai/free/en/venues/coling/) · **Publisher:** [Dublin City University](https://md.research.elamur.ai/free/en/publishers/dublin-city-university/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/C14-1151/) · [PDF](https://aclanthology.org/C14-1151.pdf) · [OpenAlex](https://openalex.org/W2251322122)

**Citations:** 134 citations (OpenAlex, 2026-10-14) · [40](https://md.research.elamur.ai/free/en/papers/basile-2014-enhanced-lesk-word-sense-disambiguation/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
The paper tackles knowledge-based, unsupervised Word Sense Disambiguation (WSD). It revisits the Lesk family of methods, whose weakness is that sense definitions (glosses) and context only match when they share identical words. The authors combine the simplified Lesk variant, which disambiguates words one at a time, with the adapted Lesk variant, which enriches each gloss with the definitions of related senses. They then replace literal word counting with similarity in a distributional space. Glosses and contexts are encoded as summed word vectors in an LSA-reduced co-occurrence space, and candidate senses are ranked by cosine similarity. Two further ingredients refine the ranking:
- an inverse gloss frequency (IGF) weighting for terms in the expanded gloss;
- an optional prior based on SemCor sense counts.

BabelNet serves as the sense inventory, which makes the method multilingual and able to handle named entities. On SemEval-2013 Multilingual WSD (Task 12), the approach beats both the most frequent sense (MFS) baseline and simplified Lesk. For English, it also surpasses every task participant. This shows that distributional similarity can make gloss-overlap methods competitive with graph-based systems.

## Key points
- **Sense lookup:** WordNet-derived BabelNet senses are tried first, with Wikipedia senses used as a fallback. The authors report that merging both sources indiscriminately lowered accuracy.
- **Gloss expansion:** glosses are extended through the BabelNet API function "getRelatedMap", and antonym-linked synsets are excluded. Expansion terms are weighted by their distance from the original synset and by IGF.
- **Final sense score:** cosine similarity is linearly combined with a sense probability p(s|w) estimated from SemCor, a corpus of 352 documents annotated with WordNet synsets.
- **Results:** both variants, with and without sense frequencies, outperform MFS and all SemEval-2013 participants for English, including the top system UMCC-DLSI. For Italian, they beat MFS but fall short of the best participant.
- **Future work:** the authors plan to cover more languages and to adapt the method to specific domains, using domain corpora to build the distributional model and to estimate sense frequencies.

*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

- [University of Bari Aldo Moro](https://md.research.elamur.ai/free/en/universities/university-of-bari-aldo-moro/) — University, Bari, Italy · 2 works · 2,185 citations

💡 Concepts (2): [🔀 Word sense disambiguation](https://md.research.elamur.ai/free/en/concepts/word-sense-disambiguation/) · 30 works · [🌫️ Distributional semantics](https://md.research.elamur.ai/free/en/concepts/distributional-semantics/) · 28 works

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 7,249 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 7,084 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 7,398 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 7,340 works

🧭 Topics (1): [🔀 Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/) · 50 works

## Cite

Cite as: Pierpaolo Basile, Annalina Caputo and Giovanni Semeraro. 2014. An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{basile2014enhanced,
  title = {{An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model}},
  author = {Pierpaolo Basile, Annalina Caputo and Giovanni Semeraro},
  booktitle = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers},
  year = {2014},
  url = {https://aclanthology.org/C14-1151/}
}
```

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## References

27 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 23 October 2026*