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<Paper uid="N01-1010">
  <Title>Tree-cut and A Lexicon based on Systematic Polysemy</Title>
  <Section position="3" start_page="1" end_page="2" type="intro">
    <SectionTitle>
Forexample, ANIMAL
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    <Paragraph position="0"> and MEAT meanings of the word \chicken&amp;quot; are related because chicken as meat refers to the esh of achicken as a bird that is used for food.  This relation is systematic, since many ANIMAL words suchas \duck&amp;quot; and \lamb&amp;quot; haveaMEAT meaning. Another example is the relation QUANTITY-PROCESSobserved in nouns such as \increase&amp;quot; and \supply&amp;quot;. Sense grouping based on systematic polysemy is lexico-semantically motivated in that it expresses general human knowledge about the relatedness of word meanings. Such sense groupings have advantages compared to other approaches. First, related senses of a word often exist simultaneously in a discourse (for example the QUANTITY and PROCESS meanings of \increase&amp;quot; above). Thus, systematic polysemy can be eectively used in WSD (and WSD evaluation) to accept multiple or alternative sense tags (Buitelaar, personal communication). Second, many systematic relations are observed between senses which belong to dierent semantic categories. So if a lexicon is dened by a collection of separate trees/hierarchies (such as the case of Word-Net), systematic polysemy can express similaritybetween senses that are not hierarchically proximate. Third, by explicitly representing(inter-)relationsbetween senses, a lexicon based on systematic polysemy can facilitate semantic inferences. Thus it is useful in knowledge-intensive NLP tasks such as discourse analysis, IE and MT. More recently, (Gonzalo et al., 2000)alsodiscussespotential usefulnessof systematic polysemy for clustering word senses for IR. However, extracting systematic relations from large sense inventories is a dicult task. Most often, this procedure is done manually. For example, WordNet cousin relations were identied manually bytheWordNet lexicographers. A similar eort was also made in the EuroWordnet project (Vossen et  Systematic polysemy (in the sense we use in this paper) is also referred to as regular polysemy (Apresjan, 1973) or logical polysemy (Pustejovsky,1995).</Paragraph>
    <Paragraph position="1">  Note that systematic polysemy should be contrasted with homonymy, which refers to words which have more than one unrelated sense (e.g. FINANCIAL INSTITUTION and SLOPING LAND meanings of the word \bank&amp;quot;).</Paragraph>
    <Paragraph position="2"> al., 1999). The problem is not only that manual inspection of a large, complex lexicon is very timeconsuming, it is also prone to inconsistencies.</Paragraph>
    <Paragraph position="3"> In this paper, we describes a lexicon organized around systematic polysemy. The lexicon is derived by a fully automatic extraction method which utilizes a clustering technique called tree-cut (Li and Abe, 1998). In our previous work (Tomuro, 2000), we applied this method to a small subset of Word-Net nouns and showed potential applicability. In the current work, we applied the method to all nouns and verbs in WordNet, and built a lexicon in which word senses are partitioned by systematic polysemy.</Paragraph>
    <Paragraph position="4"> We report results of comparing our lexicon with the WordNet cousins as well as the inter-annotator disagreement observed between two semantically annotated corpora: WordNet Semcor (Landes et al., 1998) and DSO (Ng and Lee, 1996). The results are quite promising: our extraction method discovered 89% of the WordNet cousins, and the sense partitions in our lexicon yielded better values (Carletta, 1996) than arbitrary sense groupings on the agreement data.</Paragraph>
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