<?xml version="1.0" standalone="yes"?>
<Paper uid="C00-1023">
  <Title>Word Sense Disambiguation of Adjectives Using Probabilistic Networks</Title>
  <Section position="6" start_page="156" end_page="157" type="concl">
    <SectionTitle>
5 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We have 1)resenl;etl a t)rol}~fl}ilistic disanfl}iguation model that ix sysl;e, ln~l;i{,, a(tcllral;e, all(l require llHlllual intervention ill only ill two places. The more l;illle (:OllSlllllill~ of tim l;wo manual l;asks is to (:\]assitS' th(~ toll 100 nouns needed for the priors. The el;her task~ of disanfl)igual;ing l)rol;olTpical llOllllS, is relal;ively simple due to the limited nunfl)er of glossary nouns per sense. IIowever, it would l}e straight-forward to incorporate semantically tagged corpora, such as SemCor, to avoid these mamml tasks. The priors are the number of instances of each adjective sense divided by all of the adjectives in the corpus.</Paragraph>
    <Paragraph position="1"> The disambiguated adjectiveT~i-noun#.\] pairs from the corpus ean be used as training sets to build bet{e,r ret/resental;ion of selectional preferences l}y inserting tim nounT~j node mid the ac(:omf}any featm'es into the l}elief network of a{ljectivegfii. The insertion is the same prot:c/hue used to add the hyllothe|;ical evidence dm'ing the inferoncing stage. The Ul)(lated belief networks could then be, used tbr disambigualion wii;h improve.d at:curacy. Furthernlore, the performance of BIID (:(mid a.lso be improved by exl)and null ing the context or using statistical learning methods such as the EM algorithln (DemI)ster et al., 1977).</Paragraph>
    <Paragraph position="2"> Using Bayesian networks gives the model ttexibility to incorporate additional contexts, such as syntactical and morphological features, without incurring exorbitant costs.</Paragraph>
    <Paragraph position="3"> It is l)ossible that, with an extended model that accurately disambiguates adjective-noun pairs, the selectional preference of adjective senses coutd be automatically learned. Having all improved knowledge al)out the selectional 1)references would then provide better parameters for disanfl)iguation. The model can be seen as a bootstrapping learning process tbr disambiguation, where the information gained from one part (selectional preference) is used to improve tile other (disambiguation) and vice versa, reminiscent of the work by Riloff and Jones (1.999) and Yarowsky (1995).</Paragraph>
    <Paragraph position="4"> Lastly, the techniques used in this paper could be scaled to disambiguate not only all adjective-noun pairs, but also other word pairs, such as subjectverb, verb-object, adverb-verb, by obtaining most of the paraineters from the Internet and WordNet. If the information fi'oln SemCor is also used, then the system could be automatically trained to pertbrm disambiguation tasks on all content words within a SellteI1Ce.</Paragraph>
    <Paragraph position="5"> In this paper, we have addressed three of what we believe to be the main issues timed 1)y current WSD systems. We demonstrated the effectiveness of the teclmiques used, while identii~ying two mmmal tasks that don't necessarily require a semantically tagged corpus. By establishing accurate priors a.nd small training sets, our system achieved good initial disambiguation accuracy. The salne methods could 1)e flflly automated to disami)iguate all content word pairs if infbrmation from semantically tagged corpora is used. Our goal is to create a system that can disambiguate all content words to an accuracy level sufficient for automatic tagging with tummn validation, which could then be used to improve or facilitate new probabilistic semantic taggers accurate enough for other NLP applications.</Paragraph>
  </Section>
class="xml-element"></Paper>