Conference Paper

The Effect of Ambiguity on the Automated Acquisition of WSD Examples.

Conference: Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 2-4, 2010, Los Angeles, California, USA
Source: DBLP

ABSTRACT Several methods for automatically generating labeled examples that can be used as training data for WSD systems have been proposed, including a semi-supervised approach based on relevance feedback (Stevenson et al., 2008a). This approach was shown to generate examples that improved the performance of a WSD system for a set of ambiguous terms from the biomedical domain. However, we find that this approach does not perform as well on other data sets. The levels of ambiguity in these data sets are analysed and we suggest this is the reason for this negative result.

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    ABSTRACT: Word Sense Disambiguation (WSD), the automatic identification of the meanings of ambiguous terms in a document, is an important stage in text processing. We describe a WSD system that has been developed specifically for the types of ambiguities found in biomedical documents. This system uses a range of knowledge sources. It employs both linguistic features, such as local collocations, and features derived from domain-specific knowledge sources, the Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH). This system is applied to three types of ambiguities found in Medline abstracts: ambiguous terms, abbreviations with multiple expansions and names that are ambiguous between genes. The WSD system is applied to the standard NLM-WSD data set, which consists of ambiguous terms from Medline abstracts, and was found to perform well in comparison with previously reported results. The system's performance and the contribution of each knowledge source depends upon the type of lexical ambiguity. 87.9% of the ambiguous terms are correctly disambiguated using a combination of linguistic features and MeSH terms, 99% of abbreviations are disambiguated by combining all knowledge sources, while 97.2% of ambiguous gene names are disambiguated using the MeSH terms alone. Analysis reveals that these differences are caused by the nature of each ambiguity type. These results should be taken into account when deciding which information to use for WSD and the level of performance that can be expected.
    Journal of Biomedical Informatics 12/2010; 43(6):972-81. · 2.13 Impact Factor

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