<?xml version="1.0" standalone="yes"?>
<Paper uid="H05-1067">
  <Title>Making Computers Laugh: Investigations in Automatic Humor Recognition</Title>
  <Section position="7" start_page="537" end_page="537" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> A conclusion is simply the place where you got tired of thinking.</Paragraph>
    <Paragraph position="1"> (anonymous one-liner) The creative genres of natural language have been traditionally considered outside the scope of any computational modeling. In particular humor, because of its puzzling nature, has received little attention from computational linguists. However, given the importance of humor in our everyday life, and the increasing importance of computers in our work and entertainment, we believe that studies related to computational humor will become increasingly important. null In this paper, we showed that automatic classification techniques can be successfully applied to the task of humor-recognition. Experimental results obtained on very large data sets showed that computational approaches can be efficiently used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines. To our knowledge, this is the first result of this kind reported in the literature, as we are not aware of any previous work investigating the interaction between humor and techniques for automatic classification.</Paragraph>
    <Paragraph position="2"> Finally, through the analysis of learning curves plotting the classification performance with respect to data size, we showed that the accuracy of the automatic humor-recognizer stops improving after a certain number of examples. Given that automatic humor-recognition is a rather understudied problem, we believe that this is an important result, as it provides insights into potentially productive directions for future work. The flattened shape of the curves toward the end of the learning process suggests that rather than focusing on gathering more data, future work should concentrate on identifying more sophisticated humor-specific features, e.g. semantic oppositions, ambiguity, and others. We plan to address these aspects in future work.</Paragraph>
  </Section>
class="xml-element"></Paper>