Disambiguating Noun Groupings with Respect to Wordnet Senses

Disambiguating Noun Groupings with Respect to WordNet Senses Philip Resnik Sun Microsystems Laboratories Two Elizabeth Drive Chelmsford, MA 01824-4195 USA philip, resnik@east, sun. com Abstract Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships among word senses, not words. This paper presents a method for automatic sense disambiguafion of nouns appearing within sets of related nouns -- the kind of data one finds in on-line thesauri, or as the output of distributional clustering algorithms. Disambiguation is performed with respect to WordNet senses, which are fairly fine-gained; however, the method also permits the assignment of higher-level WordNet categories rather than sense labels. The method is illustrated primarily by example, though results of a more rigorous evaluation are also presented. 1 Introduction Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional techniques become increasingly widespread (e.g. (Bensch and Savitch, 1992; Brill, 1991; Brown et al., 1992; Grefenstette, 1994; McKcown and Hatzivassiloglou, 1993; Pereira et al., 1993; Schtltze, 1993)). However, for many tasks, one is interested in relationships among word senses, not words. Consider, for example, the cluster containing attorney, counsel, trial, court, and judge, used by Brown et al. (1992) to illustrate a "semantically sticky" group of words. As is often the case where sense ambiguity is involved, we as readers impose the most coherent interpretation on the words within the group without being aware that we are doing so. Yet a computational system has no choice but to consider other, more awkward possibilities -- for example, this cluster might be capturing a distributional relationship between advice (as one sense of counsel) and royalty (as one sense of court). This would be a mistake for many applications, such as query expansion in information retrieval, where a surfeit of false connections can outweigh the benefits obtained by using lexical knowledge. One obvious solution to this problem would be to extend distributional grouping methods to word senses. For example, one could construct vector representations of senses on the basis of their co-occurrence with words or with other senses. Unfortunately, there are few corpora annotated with word sense information, and computing reliable statistics on word senses rather than words will require more data, rather than less. 1 Furthermore, one widely available example of a large, manually sense-tagged corpus -- the WordNet group's annotated subset of the Brown corpus 2 -- vividly illustrates the difficulty in obtaining suitable data. 1Actually, this depends on the fine-grainedness of sense distinctions; clearly one could annotate corpora with very high level semantic distinctions For example, Basili et al. (1994) take such a coarse-grained approach, utilizing on the order of 10 to 15 semantic tags for a given domain. I assume throughout this paper that finer-grained distinctions than that are necessary. 2Available by anonymous ftp to clarity.princeton.edu as pub/wnl. 4seracor. tar. Z; Word_Net is described by Miller et al. (1990). 54 It …

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