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<Paper uid="W00-1321">
  <Title>Reducing Parsing Complexity by Intra-Sentence Segmentation based on Maximum Entropy Model</Title>
  <Section position="8" start_page="170" end_page="170" type="concl">
    <SectionTitle>
6 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> Practical machine translation systems should be able to accommodate long sentences. Thus intra-sentence segmentation is required as a means for reducing parsing complexity. This paper presents a method for intra-sentence segmentation based on the maximum entropy model. The method builds statistical models automatically from a text corpus to provide the segmentation appropriateness for safe segmentation. null In the experiments with 1800 test sentences, about 87% of them were benefited from segmentation. The statistical intra-sentence segmentation method can also relieve human of the burden of constructing information, such as segmentation rules or sentence patterns.</Paragraph>
    <Paragraph position="1"> Experiments suggest that the proposed maximum entropy models can be incorporated into the parser for practical machine translation systems.</Paragraph>
    <Paragraph position="2"> Further works can be done in two directions. First, studies on recovery mechanisms for unsafe segmentation before parsing seem necessary since ungafe segmentation may cause parsing failures. Second, parsing control mechanisms should be studied that exploit the -characteristics of segmentation positions and the parallelism among segments. This will enhance parsing efficiency further.</Paragraph>
  </Section>
class="xml-element"></Paper>