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<Paper uid="N06-2018">
  <Title>MMR-based Active Machine Learning for Bio Named Entity Recognition</Title>
  <Section position="5" start_page="71" end_page="71" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> ction could significantly reduce the human effort.</Paragraph>
    <Paragraph position="1"> by Ministry of Commerce, Industry and Energy. s are selected.</Paragraph>
    <Paragraph position="2"> Comparing with the entropy curve, the combined strategy curve shows an interesting characteristic. Up to 4000 sentences, the entropy strategy and the combined strategy perform similarly. After the 11000 sentence point, the combined strategy surpasses the entropy strategy. It accords with our belief that the diversity increases the classifier's performance when the large amount of samples is selected. The normalized combined strategy differs from the combined strategy. It exceeds the other strategies from the beginning and maintains best performance up until 12000 sentence point. The entropy strategy reaches 67.00 in F-score when 11000 sentences are selected. The combined strategy receives 67.17 in F-score while 13000 sentences are selected, while the end performance is 67.19 using the whole training data. The combined strategy reduces 24.64 % of training examples compared with the random selection. The normalized combined strategy achieves 67.17 in F-score when 11000 sentences are selected, so 35.43% of the training examples do not need to be labeled to achieve almost the same performance as the end performance. The normalized combined strategy's performance becomes similar to the random selection strategy at around 13000 sentences, and after 14000 sentences the We incorporate active learning into the biomedical named-entity recognition system to enhance the system's performance with only small amount of training data. We presented the entropy-based uncertainty sample selection and combined selection strategies using the corpus diversity. Experiments indicate that our strategies for active-learning based sample sele</Paragraph>
  </Section>
class="xml-element"></Paper>