<?xml version="1.0" standalone="yes"?>
<Paper uid="W95-0107">
  <Title>Text Chunking using Transformation-Based Learning</Title>
  <Section position="11" start_page="92" end_page="92" type="concl">
    <SectionTitle>
8 Conclusions
</SectionTitle>
    <Paragraph position="0"> By representing text chunking as a kind of tagging problem, it becomes possible to easily apply transformation-based learning. We have shown that this approach is able to automatically induce a chunking model from supervised training that achieves recall and precision of 92% for baseNP chunks and 88% for partitioning N and V chunks. Such chunking models provide a useful and feasible next step in textual interpretation that goes beyond part-of-speech tagging, and that serve as a foundation both for larger-scale grouping and for direct extraction of subunits hke index terms. In addition, some variations in the transformation-based learning algorithm are suggested by this application that may also be useful in other settings.</Paragraph>
  </Section>
class="xml-element"></Paper>