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<Paper uid="W03-1308">
  <Title>Bio-Medical Entity Extraction using Support Vector Machines</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> The method we have shown for identifying and classifying technical terms has the advantage of being portable, not requiring large domain dependent dictionaries and no hand-made patterns were used.</Paragraph>
    <Paragraph position="1"> Additionally, since all the word level features are found automatically there is no need for intervention to create domain specific features. Indeed the only thing that is required is a quite small corpus of text containing entities tagged by a domain expert.</Paragraph>
    <Paragraph position="2"> For future work we are now looking at how to balance the scores from SVM for each word-class over the whole of a sentence using dynamic programming. Theoretically the existing SVM model cannot consider evidence from outside the context window, in particular evidence related to named entity class scores in the history and later in the sentence.</Paragraph>
  </Section>
class="xml-element"></Paper>