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<Paper uid="N04-4037">
  <Title>A Lightweight Semantic Chunking Model Based On Tagging</Title>
  <Section position="6" start_page="9978025" end_page="9978025" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have developed a novel phrase-by-phrase semantic chunker based on a non-overlapping (or chunked) shallow language structure at lexical, syntactic and semantic levels. We have implemented a baseline system and compared it to a recently proposed word-by-word system. We have shown better performance with the phrase-by-phrase approach. It has been also pointed out that the new method has several advantages; it classifies larger units, uses wider context, runs faster. Prior work has not considered this bottom-up strategy for semantic chunking, which we claim yields a lightweight, fast, and robust chunker at moderately high performance. Although we have flattened the trees in the PropBank corpus for our experiments, the proposed language structure supports flat annotation from scratch, which we believe is useful for porting the method to other domains and languages. While our initial results have been encouraging, this work must be extended and enhanced to produce the quality of semantic parse produced by systems using a full syntactic parse.</Paragraph>
  </Section>
class="xml-element"></Paper>