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<Paper uid="W00-0804">
  <Title>Experiments in Word Domain Disambiguation for Parallel Texts</Title>
  <Section position="8" start_page="32" end_page="32" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have introduced Word Domain Disambiguation, a variant of Word Sensse Disambiguation where words in a text are tagged with a domain label in place of a sense label. Two baseline algorithms has been presented as well as some extensions to deal with domain disambiguation in the context of parallel translation texts.</Paragraph>
    <Paragraph position="1"> Two aligned wordnets, the English WORDNET 1.6 and the Italian MULTIWORDNET, both augmented with domain labels, have been used as the main information repositories.</Paragraph>
    <Paragraph position="2"> The experimental results encourage to further investigate the potentiality of word domain disambiguation. There are two interesting perspectives for the future work: first, we want to exploit the relations among different lexical categories (mainly nouns and verbs) when they share the same domain label; second, it seems reasonable that the disambiguation process may take advantage of both WDD and WSD, where the initial word ambiguity is first reduced with WDD and then resolved with more fine grained information.</Paragraph>
    <Paragraph position="3"> Finally, an in-depth investigation is necessary for what we called factotum effect, which is peculiar of WDD.</Paragraph>
    <Paragraph position="4"> As for the applicative scenarios, we want to apply WDD to the problem of content based user modelling. In particular we are developing a personal agent for a news web site that learns user's interests from the requested pages that are analyzed to generate or to update a model of the user \[Strapparava et al., 2000\]. Exploiting this model, the system anticipates which documents in the web site could be interesting for the user.</Paragraph>
    <Paragraph position="5"> Using MULTIWORDNET and domain disambiguation algorithms, a content-based user model can be built as a semantic network whose nodes, independent from the language, represent the word sense frequency rather then word frequency. Furtherrnore, the resulting user model is independent from the language of the documents browsed. This is particular valuable with muitilingual web sites, that are becoming very common especially in news sites or in electronic commerce domains.</Paragraph>
  </Section>
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