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<Paper uid="C00-1023">
  <Title>Word Sense Disambiguation of Adjectives Using Probabilistic Networks</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Word sense disambiguation (WSD) remains an open probleln in Natural Language Processing (NLP). Being able to identify tile correct sense of an mnbiguous word is ilnportant for many NLP tasks, such as machine translation, information retrieval, and discourse analysis. The WSD problem is exacerbated by the large number of senses of colmnonly used words and by the difficulty in determining relevant contextual features most suitable to the task. Tile absence of semantically tagged corpora makes probabilistic techniques, shown to be very effective by speech recognition and syntactic tagging research, difficult to employ due to the sparse data problem.</Paragraph>
    <Paragraph position="1"> Early NLP systeins limited their domain and required manual knowledge engineering. More recent works take advantage of machine readable dictionaries such as WordNet (Miller, 1990) and Roget's Online Thesaurus. Statistical techniques, both supervised learning from tagged corpora (Yarowsky, 1992), (Ng and Lee, 1.996), and unsupervised learning (Yarowsky, 1995), (Resnik, 1997), have been investigated. There are also hybrid inodcls that incorporate both statistical and symbolic knowledge (Wiebe et al., 1998), (Agirre and I{igau, 1996).</Paragraph>
    <Paragraph position="2"> Supervised models have shown promising results, but the lack of sense tagged corpora often requires the need tbr laboriously tagging trailfing sets manually. Depending on the technique, unsupervised models can result in ill-defined senses. Many have not been evaluated with large vocabularies or flfll sets of senses. Hybrid models, using various heuristics, have demonstrated good accuracy but are ditficult to compare clue to variations in the evahlation procedures, as discussed in Resnik and Yarowsky (\]997).</Paragraph>
    <Paragraph position="3"> In our Bayesian Hierarchical Disambiguator (BHD) model, we attempt to address some of the main issues faced by today's WSD systelns, namely: 1) the sparse data problem; 2) the selection of a fi;ature set that can be trained upon easily without sacrificing accuracy; and 3) the scalability of the systein to disambiguate um'estricted text. The first two problems can be attributed to the lack of tagged corpora, while the third results from the need for lland-annotated text as a method of circumventing the first two problems. We will address the first two issues by identiflying contexts in which knowledge can be obtained automatically, as opposed to those that require minimal manual tagging. The effectivehess of the BHD model is then tested on unrestricted text, thus addressing the third issue.</Paragraph>
  </Section>
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