Automatic Extraction of Systematic Polysemy Using Tree-cut

Automatic Extraction of Systematic Polysemy Using Tree-cut Noriko Tomuro DePaul University School of Computer Science, Telecommunications and Information Systems 243 S. Wabash Ave. Chicago, IL 60604 tomuro@cs.depaul.edu Abstract This paper describes an automatic method for extracting systematic polysemy from a hierar- chically organized semantic lexicon (WordNet). Systematic polysemy is a set of word senses that are related in systematic and predictable ways. Our method uses a modification of a tree generalization technique used in (Li and Abe, 1998), and generates a tree-cut, which is a list of clusters that partition a tree. We compare the systematic relations extracted by our auto- matic method to manually extracted WordNet cousins. 1 Introduction In recent years, several on-line broad-coverage semantic lexicons became available, including LDOCE (Procter, 1978), WordNet (Miller, 1990) and HECTOR .(Kilgarriff, 1998a). These lexicons have been used as a domain- independent semantic resource as well as an evaluation criteria in various Natural Language Processing (NLP) tasks, such as Information Retrieval (IR), Information Extraction (IE) and Word Sense Disambiguation (WSD). However, those lexicons are rather complex. For instance, WordNet (version 1.6) contains a total of over 120,000 words and 170,000 word senses, which are grouped into around 100,000 synsets (synonym sets). In addition to the size, word entries in those lexicon are often polyse- mous. For instance, 20% of the words in Word- net have more than one sense, and the average number of senses of those polysemous words is around 3. Also, the distinction between word senses tends to be ambiguous and arbitrary. For example, the following 6 senses are listed in WordNet for the noun "door": 1. door - a swinging or sliding barrier 2. door - the space in a wall 3. door - anything providing a means of access (or escape) 4. door - a swinging or sliding barrier that will close off access into a car 5. door - a house that is entered via a door 6. door - a room that is entered via a door Because of the high degree of ambiguity, using such complex semantic lexicons brings some se- rious problems to the performance of NLP sys- tems. The first, obvious problem is the com- putational intractability: increased processing time needed to disambiguate multiple possibili- ties will necessarily slow down the system. An- other problem, which has been receiving atten- tion in the past few years, is the inaccuracy: when there is more than one sense applicable in a given context, different systems (or human in- dividuals) may select different senses as the cor- rect sense. Indeed, recent studies in WSD show that, when sense definitions are fine-grained, similar senses become indistinguishable to hu- man annotators and often cause disagreement on the correct tag (Ng et al., 1999; Veronis, 1998; Kilgarriff, 1998b). Also in IR and IE tasks, difference in the correct sense assignment will surely degrade recall and precision of the systems. Thus, it is apparent that, in order for a lexicon to be useful as an evaluation criteria for NLP systems, …

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