Abstract
WordNet is the reference sense inventory of most of the current Word Sense Disambiguation systems. Unfortunately, it encodes too fine-grained distinctions, making it difficult even for humans to solve the ambiguity of words in context. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense groups, namely the Oxford Dictionary of English. We assess the quality of the mapping and discuss the potential of the method.- Anthology ID:
- L06-1456
- Volume:
- Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
- Month:
- May
- Year:
- 2006
- Address:
- Genoa, Italy
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2006/pdf/733_pdf.pdf
- DOI:
- Cite (ACL):
- Roberto Navigli. 2006. Reducing the Granularity of a Computational Lexicon via an Automatic Mapping to a Coarse-Grained Sense Inventory. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).
- Cite (Informal):
- Reducing the Granularity of a Computational Lexicon via an Automatic Mapping to a Coarse-Grained Sense Inventory (Navigli, LREC 2006)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2006/pdf/733_pdf.pdf