![Word sense disambiguation of WordNet glosses [An article from: Computer Speech & Language]](https://www.ebooknetworking.net/books/B00/0RR/bigB000RR0SD0.jpg)
Word sense disambiguation of WordNet glosses [An article from: Computer Speech & Language]
Book Details
Author(s)D. Moldovan, A. Novischi
PublisherElsevier
ISBN / ASINB000RR0SD0
ISBN-13978B000RR0SD2
AvailabilityAvailable for download now
Sales Rank14,691,218
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Computer Speech & Language, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
This paper presents a suite of methods and results for the semantic disambiguation of WordNet glosses. WordNet is a resource widely used in natural language processing and artificial intelligence. Intended and designed as a lexical database, WordNet exhibits some deficiencies when used as a knowledge base. By semantically disambiguating the words in the glosses, we add pointers from each word to its concept or synset, and this increases the connectivity between the WordNet concepts by approximately an order of magnitude. We show how lexical chains and other applications can be built on this richly connected WordNet. The semantic disambiguation of the WordNet glosses is performed using automatic methods based on a set of heuristics. The precision of the semantic annotation is improved by using voting between the disambiguation system described here and another WSD system. The entire WordNet 2.0 has been disambiguated with an overall precision of 86% and is available at http://xwn.hlt.utdallas.edu.
Description:
This paper presents a suite of methods and results for the semantic disambiguation of WordNet glosses. WordNet is a resource widely used in natural language processing and artificial intelligence. Intended and designed as a lexical database, WordNet exhibits some deficiencies when used as a knowledge base. By semantically disambiguating the words in the glosses, we add pointers from each word to its concept or synset, and this increases the connectivity between the WordNet concepts by approximately an order of magnitude. We show how lexical chains and other applications can be built on this richly connected WordNet. The semantic disambiguation of the WordNet glosses is performed using automatic methods based on a set of heuristics. The precision of the semantic annotation is improved by using voting between the disambiguation system described here and another WSD system. The entire WordNet 2.0 has been disambiguated with an overall precision of 86% and is available at http://xwn.hlt.utdallas.edu.
