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


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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Available from: Mark Stevenson, Oct 05, 2015
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    • "Stevenson and Guo [33] also describe an approach that relies on computing the average pairwise similarity between the possible senses of ambiguous terms (see Section 2.2.1). Like counting the number of possible senses, this approach also has the advantage of not requiring any labeled training data. "
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    ABSTRACT: Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of WSD systems is often expensive with respect to acquiring the necessary training data. It would therefore be useful to be able to predict in advance which terms WSD systems are likely to perform well or badly on. This paper explores various methods for estimating the performance of WSD systems on a wide range of ambiguous biomedical terms (including ambiguous words/phrases and abbreviations). The methods include both supervised and unsupervised approaches. The supervised approaches make use of information from labeled training data while the unsupervised ones rely on the UMLS Metathesaurus. The approaches are evaluated by comparing their predictions about how difficult disambiguation will be for ambiguous terms against the output of two WSD systems. We find the supervised methods are the best predictors of WSD difficulty, but are limited by their dependence on labeled training data. The unsupervised methods all perform well in some situations and can be applied more widely.
    Journal of Biomedical Informatics 09/2013; 47. DOI:10.1016/j.jbi.2013.09.009 · 2.19 Impact Factor
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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. DOI:10.1016/j.jbi.2010.08.009 · 2.19 Impact Factor