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<Paper uid="W04-0829">
  <Title>WSD Based on Mutual Information and Syntactic Patterns</Title>
  <Section position="3" start_page="0" end_page="0" type="metho">
    <SectionTitle>
3 Syntactic patterns
</SectionTitle>
    <Paragraph position="0"> This heuristic exploits the regularity of syntactic patterns in sense disambiguation. These repetitive patterns effectively exist, although they might correspond to different word meanings . One example is the pattern in figure 1 which usually corresponds to a specific sense of art in the SENSEVAL-2 English lexical sample task.</Paragraph>
    <Paragraph position="1"> This regularities can be attached to different degrees of specificity. One system that made use of these regularities is (Tugwell and Kilgarriff, 2001). The regularities were determined by human interaction with the system. We have taken a different approach, so that the result is a fully automatic system. As in the previous heuristic, we didn't take into consideration the senses with a relative frequency below 10%.</Paragraph>
    <Paragraph position="2"> Due to time constraints we couldn't devise a method to identify salient syntactic patterns useful for WSD, although the task seems challenging. Instead, we parsed the examples in WordNet glosses. These examples are usually just phrases, not complete sentences, but they can be used as patterns straightaway. We parsed the test instances as well and looked for matches of the example inside the parse tree of the test instance. Coverage was very low. In order to increase it, we adopted the following strategy : To take a gloss example and go down the parse tree looking for the word to disambiguate. The subtrees of the visited nodes are smaller and smaller. Matching the whole syntactic tree of the example is rather unusual but chances increase with each of the subtrees. Of course, if we go too low in the tree we will be left with the single target word, which should in principle match all the correspond- null ing trees of the test items of the same word. We will illustrate the idea with an example. An example of an art sense gloss is : Architecture is the art of wasting space beatifully. We can see the parse tree depicted in figure 2.</Paragraph>
    <Paragraph position="3"> We could descend from the root, looking for the occurrence of the target word and obtain a second, simpler, pattern, shown in figure 3.</Paragraph>
    <Paragraph position="4"> Following the same procedure we would acquire the patterns shown in figures 4 y 5, and the we would be left with mostly useless pattern shown in figure 6 Since there is an obvious tradeoff between coverage and precision, we have only made disambiguation rules based on the first three syntactic levels, and rejected rules with a pattern with only one word. Still, coverage seems to be rather low and there are areas of the pattern that look like they could be generalized without much loss of precision, even when it might be difficult to identify them. Our  hypothesis is that function words play an important role in the discovery of these syntactic patterns. We had no time to further investigate the fine-tuning of these patterns, so we added a series of transformations for the rules already obtained. In the first place, we replaced every tagged pronoun form with a wildcard meaning that every word tagged as a pronoun would match. In order to increase even more the number of rules we derive more rules keeping the part-of-speech tags and replacing content words with wilcards.</Paragraph>
    <Paragraph position="5"> We wanted to derive a larger set of rules, with the two-fold intention of achieving increased coverage and also to test if the approach was feasible with a rule set in the order of the hundreds of thousands or even millions. Every rule specifies the word for which it is applicable (for the sake of efficiency) and the sense the rule supports, as well as the syntactic pattern. We derived new rules in which we substituted the word to be disambiguated for each of its variants in the corresponding sense (i.e. the synonyms in the corresponding synset). The substitution was carried out sensibly in all the four fields of the rule, with the new word-sense (corresponding to  the same synset as the old one), the new variant and the new syntactic pattern. This way we were able to effectively multiply the size of the rule set.</Paragraph>
    <Paragraph position="6"> We have also derived a set of disambiguation rules based on the training examples for the English lexical sample task. The final rule set consists of more than 300000 rules. The score for a sense is determined by the total number of rules it matches.</Paragraph>
    <Paragraph position="7"> We only take the sense with the highest score.</Paragraph>
    <Paragraph position="8"> The results of the evaluation for this heuristic are shown in table 2</Paragraph>
  </Section>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
4 Combination
</SectionTitle>
    <Paragraph position="0"> Since we are interested in achieving a high recall and both our heuristics have low coverage, we decided to combine the results in a blind way with the first sense heuristic. We did a linear combination of the three heuristics, weighting the three of them equally, and returned the sense with the highest score.</Paragraph>
  </Section>
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