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<Paper uid="P04-1075">
  <Title>Multi-Criteria-based Active Learning for Named Entity Recognition</Title>
  <Section position="6" start_page="0" end_page="0" type="relat">
    <SectionTitle>
5 Related Work
</SectionTitle>
    <Paragraph position="0"> Since there is no study on active learning for NER task previously, we only introduce general active learning methods here. Many existing active learning methods are to select the most uncertain examples using various measures (Thompson et al. 1999; Schohn and Cohn 2000; Tong and Koller 2000; Engelson and Dagan 1999; Ngai and Yarowsky 2000). Our informativeness-based measure is similar to these works. However these works just follow a single criterion. (McCallum and Nigam 1998; Tang et al. 2002) are the only two works considering the representativeness criterion in active learning. (Tang et al. 2002) use the density information to weight the selected examples while we use it to select examples. Moreover, the representativeness measure we use is relatively general and easy to adapt to other tasks, in which the example selected is a sequence of words, such as text chunking, POS ta gging, etc. On the other hand, (Brinker 2003) first incorporate diversity in active learning for text classification. Their work is similar to our local consideration in Section 2.3.2.</Paragraph>
    <Paragraph position="1"> However, he didn't further explore how to avoid selecting outliers to a batch. So far, we haven't found any previous work integrating the informativeness, representativeness and diversity all together. null</Paragraph>
  </Section>
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