Automatic Identification of Infrequent Word Senses

Automatic Identification of Infrequent Word Senses Diana McCarthy & Rob Koeling & Julie Weeds & John Carroll Department of Informatics, University of Sussex Brighton BN1 9QH, UK � dianam,robk,juliewe,johnca � @sussex.ac.uk Abstract In this paper we show that an unsupervised method for ranking word senses automatically can be used to iden- tify infrequently occurring senses. We demonstrate this using a ranking of noun senses derived from the BNC and evaluating on the sense-tagged text available in both SemCor and the SENSEVAL-2 English all-words task. We show that the method does well at identifying senses that do not occur in a corpus, and that those that are erro- neously filtered but do occur typically have a lower fre- quency than the other senses. This method should be useful for word sense disambiguation systems, allowing effort to be concentrated on more frequent senses; it may also be useful for other tasks such as lexical acquisition. Whilst the results on balanced corpora are promising, our chief motivation for the method is for application to do- main specific text. For text within a particular domain many senses from a generic inventory will be rare, and possibly redundant. Since a large domain specific cor- pus of sense annotated data is not available, we evaluate our method on domain-specific corpora and demonstrate that sense types identified for removal are predominantly senses from outside the domain. 1 Introduction Much about the behaviour of words is most appro- priately expressed in terms of word senses rather than word forms. However, an NLP application computing over word senses is faced with consid- erable extra ambiguity. There are systems which can perform word sense disambiguation (WSD) on the words in input text, however there is room for improvement since the best systems on the English SENSEVAL-2 all-words task obtained at most 69% for precision and recall. Whilst there are systems that obtain higher precision (Magnini et al., 2001), these typically suffer from a low recall. WSD per- formance is affected by the degree of polysemy, but even more so by the entropy of the frequency distri- butions of the words’ senses (Kilgarriff and Rosen- zweig, 2000) since the distribution for many words is highly skewed. Many of the senses in such an inventory are rare and WSD and lexical acquisition systems do best when they take this into account. There are many ways that the skewed distribution can be taken into account. One successful approach is to back-off to the first (predominant) sense (Wilks and Stevenson, 1998; Hoste et al., 2001). Another possibility would be concentrate the selection pro- cess to senses with higher frequency, and filter out rare senses. This is implicitly done by systems which rely on hand-tagged training corpora, since rare senses often do not occur in the available data. In this paper we use an unsupervised method to rank word senses from an inventory according to preva- lence (McCarthy et al., 2004a), and utilise the rank- ing scores to identify senses which are rare. We use …

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