Abstract
WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples. Most of the approaches to the Word Sense Disambiguation task fully rely on these short texts as a source of contextual information to match with the input text to disambiguate. This paper presents the first attempt to enrich synsets data with common-sense definitions, automatically retrieved from ConceptNet 5, and disambiguated accordingly to WordNet. The aim was to exploit the shared- and immediate-thinking nature of common-sense knowledge to extend the short but incredibly useful contextual information of the synsets. A manual evaluation on a subset of the entire result (which counts a total of almost 600K synset enrichments) shows a very high precision with an estimated good recall.- Anthology ID:
- L16-1132
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 819–822
- Language:
- URL:
- https://aclanthology.org/L16-1132
- DOI:
- Cite (ACL):
- Luigi Di Caro and Guido Boella. 2016. Automatic Enrichment of WordNet with Common-Sense Knowledge. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 819–822, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Automatic Enrichment of WordNet with Common-Sense Knowledge (Di Caro & Boella, LREC 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/L16-1132.pdf