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<Paper uid="P03-2017">
  <Title>Towards Interactive Text Understanding</Title>
  <Section position="6" start_page="5" end_page="5" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have introduced an interactive approach to text understanding, based on an extension to the MDA document authoring system. ITU at this point is more a research program than a completed realization. However we think it represents an exciting direction towards permitting a reliable deep semantic analysis of input documents by complementing automatic information  Let us briefly mention that we are not the first to note formal connections between natural language understanding and statistical MT. Thus, [Epstein 1996], working in a non-interactive framework, draws the following parallel between the two tasks: while in MT, the aim is to produce a target text from a source text, in NLU, the aim is to produce a semantic representation from an input text. He then goes on to adapt the conventional noisy channel MT model of [Brown et al 1993] to NLU, where extracting a semantic representation from an input text corresponds to finding: argmax(Sem) {p(Input|Sem) p(Sem)}, where p(Sem) is a model for generating semantic representations, and p(Input|Sem) is a model for the relation between semantic representations and corresponding texts. See also [Berger and Lafferty 1999] and [Knight and Marcu 2002] for parallels between statistical MT and Information Retrieval and Summarization respectively. On a different plane, in the context of interactive NLG, [Nickerson 2003] has recently proposed to rank semantic choices according to probabilities estimated from a corpus; but here the purpose is not text understanding, but improving the speed of authoring a new document from scratch.</Paragraph>
    <Paragraph position="1"> extraction with a minimal amount of human intervention for those aspects of understanding that presently resist automation.</Paragraph>
  </Section>
class="xml-element"></Paper>