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<Paper uid="P97-1062">
  <Title>Learning Parse and Translation Decisions</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The parsing of unrestricted text, with its enormous lexical and structural ambiguity, still poses a great challenge in natural language processing. The traditional approach of trying to master the complexity of parse grammars with hand-coded rules turned out to be much more difficult than expected, if not impossible. Newer statistical approaches with often only very limited context sensitivity seem to have hit a performance ceiling even when trained on very large corpora.</Paragraph>
    <Paragraph position="1"> To cope with the complexity of unrestricted text, parse rules in any kind of formalism will have to consider a complex context with many different morphological, syntactic or semantic features. This can present a significant problem, because even linguistically trained natural language developers have great difficulties writing and even more so extending explicit parse grammars covering a wide range of natural language. On the other hand it is much easier for humans to decide how specific sentences should be analyzed.</Paragraph>
    <Paragraph position="2"> We therefore propose an approach to parsing based on learning from examples with a very strong emphasis on context, integrating morphological, syntactic, semantic and other aspects relevant to making good parse decisions, thereby also allowing the parsing to be deterministic. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce type parser in the form of a decision structure. The generated parser transforms input sentences into an integrated phrase-structure and case-frame tree, powerful enough to be fed into a transfer and a generation module to complete the full process of machine translation.</Paragraph>
    <Paragraph position="3"> Balanced by rich context and some background knowledge, our corpus based approach relieves the NL-developer from the hard if not impossible task of writing explicit grammar rules and keeps grammar coverage increases very manageable. Compared with standard statistical methods, our system relies on deeper analysis and more supervision, but radically fewer examples.</Paragraph>
  </Section>
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