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<Paper uid="W98-0710">
  <Title>Aligning WordNet with Additional Lexical Resources</Title>
  <Section position="6" start_page="75" end_page="78" type="evalu">
    <SectionTitle>
5 Discussion
</SectionTitle>
    <Paragraph position="0"> Though the actual figures in Table 5 may not be significant in themselves given the small s~unple size in the test, they nevertheless indicate some underlying relationships among the resources, and suggest it will be worth pursuing larger scale tests.</Paragraph>
    <Section position="1" start_page="75" end_page="75" type="sub_section">
      <SectionTitle>
5.1 Resource Similarities and Differences
</SectionTitle>
      <Paragraph position="0"> Results from the first two parts of the inwestigation give us a rough idea of the similarity and difference among the resources. Comparing the overall correlation between WN and LDOCE with respect to the number of senses per word with that between WN and ROGET, the much higher positive correlation found for the former suggests that WN, though organised like a thesaurus, its content is like that in a dictionary.</Paragraph>
      <Paragraph position="1"> While the correlation results give us an idea of how strong the linear relationship is, the t-test results suggest to us that a conventional dictionary seems to capture relatively more meanings than WN when a word has fewer than five WN senses. On the other hand, a similar relation was found between WN and ROGET for words which have more than 10 WN senses. However, this could mean two things: either that WN does contain more information than a thesaurus, or that the WN senses are getting relatively more fine-grained.</Paragraph>
      <Paragraph position="2"> In the experiment we divided the test nouns into three groups of different degree of polyserny. However, a rough count from WordNet 1.5 reveals that out of the 88200 nouns, only 0.07% belongs to the HI group, with an average of 13.18 senses; whereas 0.55% belongs to the MED group, with an average of 7.05 senses. Up to 99.37% of the nouns come from the LO group, averaging 1.18 senses. In other words, the idiosyncrasy found for the HI group may have been magnified in the test samples, and we can expect in general a rather consistent relatiorLship between WN and the other two resources.</Paragraph>
    </Section>
    <Section position="2" start_page="75" end_page="77" type="sub_section">
      <SectionTitle>
5.2 Aligning WN senses with others
</SectionTitle>
      <Paragraph position="0"> The third part reveals more details of the interrelationship among the resources. Knight and Luk (1994) reported a trade-off between coverage and correctness. Our results, albeit for a smaller test and with different ambiguity grouping, are comparable with theirs. Thus our Accurately Mapped figures correspond effectively to their pct correct at their confidence level &gt; 0.0. A similar average of slightly more than 60% accuracy was achieved.</Paragraph>
      <Paragraph position="1"> Overall, the Accurately Mapped figures support our hypothesised structural relationship between a conventional dictionary, a thesaurus and WN, showing that we can use this method to align senses in one resource with those in another. As we expected, no statistically significant difference was found for accuracy across the three groups of words. This would mean that the algorithm gives similar success rates regardless of how many meanings a word has.</Paragraph>
      <Paragraph position="2"> In addition, we have also analysed the unsuccessful cases into four categories as shown earlier. It can be seen that &amp;quot;false alarms&amp;quot; were more prevalent than &amp;quot;misses&amp;quot;, showing that errors mostly arise from the inadequacy of individual resources because there are no targets rather than from failures of the mapping process. Moreover, the number of &amp;quot;misses&amp;quot; can possibly be reduced, for example, by a better way to identify genus terms, or if more definition patterns are considered.</Paragraph>
      <Paragraph position="3"> Forced Error refers to cases without any satisfactory target, but somehow one or more other targets score higher than the rest. We see that this figure is significantly higher in the HI group than in the other two groups for the mappings between WN and RO-GET, showing that there are relatively more senses in WN which can find no counterpart in ROGET. So WN does have something not captured by ROGET.</Paragraph>
      <Paragraph position="4"> The polysemy factor 79 can also tell us something regarding how fine-grained the senses are in one resource with respect to the other. The significantly lower 79 in the HI group implies that as more meanings are listed for a word, these meanings can nevertheless be grouped into just a few core meanings.</Paragraph>
      <Paragraph position="5"> Unless we require very detailed distinction, a cruder discrimination would otherwise suffice.</Paragraph>
      <Paragraph position="6">  Thus, the Forced Error and Polysemy Factor data show that both &amp;quot;more information&amp;quot; (in the sense of more coverage of the range of uses of a word) and &amp;quot;more granularity&amp;quot; contribute to the extra; senses in WN in the HI group. However, no precise conclusion can be drawn because this is rather variable even within one resource.</Paragraph>
      <Paragraph position="7"> Another observation was made regarding: the mapping between WN and ROGET. Unlike the: mapping between LDOCE and WN which is easy to check by comparing the definitions, synonyms and so on, judging whether a mapping from WN to ROGET is correct is not always straightforward. This is because either the expected target and the mapped target are not identical but are nevertheless close neighbours in the Roget class hierarchy, o:r because different targets would be expected depending on which part of the definition one's focus is on. For instance, &amp;quot;cast&amp;quot; has the sense of &amp;quot;the actors in a play&amp;quot;. Strictly speaking it should be put under &amp;quot;assemblage', but we may be unwilling to say a mapping to &amp;quot;drama&amp;quot; is wrong. As we have said, WN and ROGET have different classificatory structures.</Paragraph>
      <Paragraph position="8"> Nevertheless, we may be able to take adw~tage of this difference as discussed in the next section.</Paragraph>
    </Section>
    <Section position="3" start_page="77" end_page="78" type="sub_section">
      <SectionTitle>
5.3 Making use of the findings
</SectionTitle>
      <Paragraph position="0"> Clearly successful mappings are influenced by the fineness of the sense discrimination in the resources.</Paragraph>
      <Paragraph position="1"> How finely they are distinguished can be inferred from the similarity score matrices generated from the algorithm for the two pairs of mappings. Reading the matrices row-wise shows how vaguely a certain sense is defined, whereas reading them columnwise reveals how polysemous a word is. The presence of unattached senses also implies that using only one single resource in any NLP application is likely to be insufficient.</Paragraph>
      <Paragraph position="2"> This is illustrated for one of the test words (note) in Figure 2. (. = correctly mapped, o = ,expected target, x = incorrectly mapped, and * = fi~rced error) Tables 6 and ? show the corresponding WN synsets and LDOCE senses. It can be seen that Dt and D~_ are both about musical notes, whereas D~ and Ds can both be construed as letters.</Paragraph>
      <Paragraph position="3"> Consequently, using this kind of mapping data, we may be able to overcome the inadequacy of WN in at least two ways: (i) supplementing the missing senses to achieve an overall balanced sense discrimination, and (ii) superimposing the WN taxonomy with another semantic classification scheme such as that found in ROGET.</Paragraph>
      <Paragraph position="4"> For the first proposal, we can, for example, conflare the mapped senses and complement them with the detached ones, thus resulting in a more complete but not redundant sense discrimination. In the above case, we can obtain the following new set of senses for note:  note, promissory note, note of hand bill, note, government note, bank bill, banker's bill, bank note, banknote, Federal Reserve note, greenback note (tone of voice)  1. promissory note 2. bank note 3. tone of voice 4. musical note 5. annotation 6. letter 7. vritten record 8. eminence 9. emotional quality 10. element  Note that 1 to 7 are the senses mapped and continted, 8 and 9 are the unattached syusets in WN, and 10 is the unattached sense in LDOCE. The second proposal is based on the observation that the classificatory structures in WN and ROGET may be used to complement each other because each of them may provide a better way to capture semantic information in a text at different times. As in our &amp;quot;cast&amp;quot; example, the WN taxonomy allows prop-erty inheritance and other logical inference from the information that &amp;quot;cast&amp;quot; is an assemblage, and thus is a social group; while the ROGET classification also captures the :'drama&amp;quot; setting, so that we know it is not just any group of people, but only those involved in drama. Imagine we have another situation as follows: He sang a song last night. The notes were too high for a bass.</Paragraph>
      <Paragraph position="5"> The hypernym chains for the underlined nouns in WN are as follows (assuming that we have spotted the intended senses):  a record or reminder in writing a remark added to a piece of writing and placed outside the main part of the writing (a~ at the side or bottom of a pa~e, or at the emi) a short usu. informal letter a formal letter between governments a'piece of paper money  Again, it is important that bass should be able to inherit the properties from person or note from written communication, and so on, as WN now allows us to do. But at the same time, it can be seen that the nouns can hardly be related to one another in the WN hierarchical structure except at the top node entity, and it is then difficult to tell what the discourse is about. However, if we align the senses with the ROGET classes, we can possibly solve the problem. Consequently, the details of how we can fle~dbly use the two classifications together can be a future direction of this research.</Paragraph>
    </Section>
  </Section>
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