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<Paper uid="P97-1023">
  <Title>Predicting the Semantic Orientation of Adjectives</Title>
  <Section position="9" start_page="178" end_page="179" type="concl">
    <SectionTitle>
10 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We have proposed and verified from corpus data constraints on the semantic orientations of conjoined adjectives. We used these constraints to automatically construct a log-linear regression model, which, combined with supplementary morphology rules, predicts whether two conjoined adjectives are of same 812 links per adjective for a set of n adjectives requires  In each figure, the last z coordinate indicates the (average) maximum possible value of k for this P, and the dotted line shows the performance of a random classifier.</Paragraph>
    <Paragraph position="1"> or different orientation with 82% accuracy. We then classified several sets of adjectives according to the links inferred in this way and labeled them as positive or negative, obtaining 92% accuracy on the classification task for reasonably dense graphs and 100% accuracy on the labeling task. Simulation experiments establish that very high levels of performance can be obtained with a modest number of links per word, even when the links themselves are not always correctly classified.</Paragraph>
    <Paragraph position="2"> As part of our clustering algorithm's output, a &amp;quot;goodness-of-fit&amp;quot; measure for each word is computed, based on Rousseeuw's (1987) silhouettes.</Paragraph>
    <Paragraph position="3"> This measure ranks the words according to how well they fit in their group, and can thus be used as a quantitative measure of orientation, refining the binary positive-negative distinction. By restricting the labeling decisions to words with high values of this measure we can also increase the precision of our system, at the cost of sacrificing some coverage.</Paragraph>
    <Paragraph position="4"> We are currently combining the output of this system with a semantic group finding system so that we can automatically identify antonyms from the corpus, without access to any semantic descriptions.</Paragraph>
    <Paragraph position="5"> The learned semantic categorization of the adjectives can also be used in the reverse direction, to help in interpreting the conjunctions they participate. We will also extend our analyses to nouns and verbs.</Paragraph>
  </Section>
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