Reducing the Granularity of a Computational Lexicon via an Automatic Mapping to a Coarse-Grained Sense Inventory

Roberto Navigli


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
Editors:
Nicoletta Calzolari, Khalid Choukri, Aldo Gangemi, Bente Maegaard, Joseph Mariani, Jan Odijk, Daniel Tapias
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:
Bibkey:
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)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2006/pdf/733_pdf.pdf