<?xml version="1.0" standalone="yes"?>
<Paper uid="P04-1048">
  <Title>Inducing Frame Semantic Verb Classes from WordNet and LDOCE</Title>
  <Section position="10" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> We have demonstrated that sets of verbs evoking a common semantic frame can be induced from existing lexical tools. In a head-to-head comparison with frames in FrameNet, the frame semantic verb classes developed by the SemFrame approach achieve a recall of 83.2% and the verbs listed for frames achieve a precision of 73.8%; these results far outpace those of other semantic verb classes. On a practical level, a large number of frame semantic verb classes have been identified. Associated with clustering threshold 1.5 are 1421 verb classes, averaging 14.1 WordNet verb synsets. Associated with clustering threshold 2.0 are 1563 verb classes, averaging 6.6 WordNet verb synsets.</Paragraph>
    <Paragraph position="1"> Despite these promising results, we are limited by the scope of our input data set. While LDOCE and WordNet data are generally of high quality, the relative sparseness of these resources has an adverse impact on recall. In addition, the mapping technique used for picking out corresponding word senses in WordNet and LDOCE is shallow, thus constraining the recall and precision of SemFrame outputs. Finally, the multi-step process of merging smaller verb groups into verb groups that are intended to correspond to frames sometimes fails to achieve an appropriate degree of correspondence (all the verb classes discovered are not distinct).</Paragraph>
    <Paragraph position="2"> Lin and Pantel have taken a similar approach,4 &amp;quot;naming&amp;quot; their verb clusters by the first three verbs listed for a cluster, i.e., the three most similar verbs. In our future work, we will experiment with the more recent release of WordNet (2.0). This version provides derivational morphology links between nouns and verbs, which will promote far greater precision in the linking of verb senses based on morphology than was possible in our initial implementation. Another significant addition to WordNet 2.0 is the inclusion of category domains, which co-locate words pertaining to a subject and perform the same function as LDOCE's subject field codes.</Paragraph>
    <Paragraph position="3"> Finally, data sparseness issues may be addressed by supplementing the use of the lexical resources used here with access to, for example, the British National Corpus, with its broad coverage and carefully-checked parse trees.</Paragraph>
  </Section>
class="xml-element"></Paper>