Discovering Corpus-Specific Word Senses

Discovering Corpus-Specific Word Senses Beate Dorow Institut fiir Maschinelle Sprachverarbeitung Universita Stuttgart, Germany beate.dorow@ims.uni-stuttgart.de Dominic Widdows Center for the Study of Language and Information Stanford University, California dwiddows@csli.stanford.edu Abstract This paper presents an unsupervised al- gorithm which automatically discovers word senses from text. The algorithm is based on a graph model representing words and relationships between them. Sense clusters are iteratively computed by clustering the local graph of similar words around an ambiguous word. Dis- crimination against previously extracted sense clusters enables us to discover new senses. We use the same data for both recognising and resolving ambigu- ity. 1 Introduction This paper describes an algorithm which automa- tically discovers word senses from free text and maps them to the appropriate entries of existing dictionaries or taxonomies. Automatic word sense discovery has applica- tions of many kinds. It can greatly facilitate a lexi- cographer's work and can be used to automatically construct corpus-based taxonomies or to tune ex- isting ones. The same corpus evidence which sup- ports a clustering of an ambiguous word into dis- tinct senses can be used to decide which sense is referred to in a given context (Schiitze, 1998). This paper is organised as follows. In section 2, we present the graph model from which we dis- cover word senses. Section 3 describes the way we divide graphs surrounding ambiguous words into different areas corresponding to different senses, using Markov clustering (van Dongen, 2000). The quality of the Markov clustering depends strongly on several parameters such as a granularity factor and the size of the local graph. In section 4, we outline a word sense discovery algorithm which bypasses the problem of parameter tuning. We conducted a pilot experiment to examine the per- formance of our algorithm on a set of words with varying degree of ambiguity. Section 5 describes the experiment and presents a sample of the re- sults. Finally, section 6 sketches applications of the algorithm and discusses future work. 2 Building a Graph of Similar Words The model from which we discover distinct word senses is built automatically from the British Na- tional corpus, which is tagged for parts of speech. Based on the intuition that nouns which co-occur in a list are often semantically related, we extract contexts of the form Noun, Noun,... and/or Noun, e.g. "genomic DNA from rat, mouse and dog". Following the method in (Widdows and Dorow, 2002), we build a graph in which each node repre- sents a noun and two nodes have an edge between them if they co-occur in lists more than a given number of times 1 . Following Lin's work (1998), we are cur- rently investigating a graph with verb-object, verb-subject and modifier-noun-collocations from which it is possible to infer more about the senses of systematically polysemous words. The word sense clustering algorithm as outlined below can be applied to any kind of similarity measure based on any set of features. 1 Si mple cutoff functions proved unsatisfactory because of the bias they give …

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