KUNLP system in Senseval-3

KUNLP System in SENSEVAL-3 Hee-Cheol Seo, Hae-Chang Rim Dept. of Computer Science and Engineering, Korea University 1, 5-ka, Anam-dong, Seongbuk-Gu, Seoul, 136-701, Korea �hcseo, rim�@nlp.korea.ac.kr Soo-Hong Kim Dept. of Computer Software Engineering, College of Engineering, Sangmyung University, San 98-20, Anso-Dong, Chonan, Chungnam, Korea soohkim@smuc.ac.kr Abstract We have participated in both English all words 3.task and English lexical sample task of SENSEVAL- Our system disambiguates senses of a target word in a context by selecting a substituent among WordNet relatives of the target word, such as syn- onyms, hypernyms, meronyms and so on. The deci- sion is made based on co-occurrence frequency be- tween candidate relatives and each of the context words. Since the co-occurrence frequency is obtain- able from raw corpus, our method is considered to be an unsupervised learning algorithm that does not require a sense-tagged corpus. 1 Introduction At SENSEVAL-3, we adopted an unsupervised ap- proach based on WordNet and raw corpus, which does not require any sense tagged corpus. Word- Net specifies relationships among the meanings of words. Relatives of a word in WordNet are defined as words that have a relationship with it, e.g. they are synonyms, antonyms, superordinates (hyper- nyms), or subordinates (hyponyms). Relatives, es- pecially those in a synonym class, usually have related meanings and tend to share similar con- texts. Hence, some WordNet-based approaches ex- tract relatives of each sense of a polysemous word from WordNet, collect example sentences of the rel- atives from a raw corpus, and learn the senses from the example sentences for WSD. Yarowsky (1992) first proposed this approach, but used International Roget’s Thesaurus as a hierarchical lexical database instead of WordNet. However, the approach seems to suffer from examples irrelevant to the senses of a polysemous word since many of the relatives are polysemous. Leacock et al. (1998) attempted to ex- clude irrelevant or spurious examples by using only monosemous relatives in WordNet. However, some senses do not have short distance monosemous rel- atives through a relation such as synonym, child, and parent. A possible alternative of using only monosemous relatives in the long distance, how- ever, is problematic because the longer the distance of two synsets in WordNet, the weaker the relation- ship between them. In other words, the monose- mous relatives in the long distance may provide ir- relevant examples for WSD. Our approach is somewhat similar to the Word- Net based approach of Leacock et al. (1998) in that it acquires relatives of a target word from WordNet and extracts co-occurrence frequencies of the rela- tives from a raw corpus, but our system uses poly- semous as well as monosemous relatives. To avoid a negative effect of polysemous relatives on the co- occurrence frequency calculation, our system han- dles the example sentences of each relative sepa- rately instead of putting together the example sen- tences of all relatives into a pool. Also we devised our system to efficiently disambiguate senses of all words using only co-occurrence frequency between words. 2 KUNLP system 2.1 Word Sense …

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