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<Paper uid="W97-0808">
  <Title>Word Sense Disambiguation for Acquisition of Selectional Preferences</Title>
  <Section position="8" start_page="68" end_page="68" type="concl">
    <SectionTitle>
4 Conclusions
</SectionTitle>
    <Paragraph position="0"> From inspection of the ATCMs obtained so far it appears that even crude WSD does help the selectional preference acquisition especially in cases of sparse data, however this still needs formal evaluation to verify whether the difference is significant. WSD is particularly useful when the quantity of data is small as is the case when collecting data for a  specific predicate. WSD selecting the most frequent sense regardless of context certainly seems to help overall despite mistakes. The preferences are improved still further if art iterative approach is taken and the preferences produced with initial WSD are used to disambiguate the heads which cart then be fed back into the preference acquisition system. This has the effect of removing preferences caused by erroneous senses.</Paragraph>
    <Paragraph position="1"> So far experiments using Yarowsky's unsupervised algorithm take too long for training each word to produce semantic tagging of sufficient quantity of text for preference acquisition but may be useful for disambiguation of target verbs, particularly with adaptations to aLlow a coarser grarmlarity than the exact WordNet sense.</Paragraph>
  </Section>
class="xml-element"></Paper>