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<Paper uid="P04-1075">
  <Title>Multi-Criteria-based Active Learning for Named Entity Recognition</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> In this paper, we study the active learning in a more complex NLP task, named entity recognition.</Paragraph>
    <Paragraph position="1"> We propose a multi-criteria -based approach to select examples based on their informativeness, representativeness and diversity, which are incorporated all together by two strategies (local and global). Experiments show that, in both MUC-6 and GENIA, both of the two strategies combining the three criteria outperform the single criterion (informativeness). The labeling cost can be significantly reduced by at least 80% comparing with the supervised learning. To our best knowledge, this is not only the first work to report the empir ical results of active learning for NER, but also the first work to incorporate the three criteria all together for selecting examples.</Paragraph>
    <Paragraph position="2"> Although the current experiment results are very promising, some parameters in our experiment, such as the batch size K and the l in the function of strategy 2, are decided by our experience in the domain. In practical application, the optimal value of these parameters should be decided automatically based on the training process.</Paragraph>
    <Paragraph position="3"> Furthermore, we will study how to overcome the limitation of the strategy 1 discussed in Section 3 by using more effective clustering algorithm. Another interesting work is to study when to stop active learning.</Paragraph>
  </Section>
class="xml-element"></Paper>