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<Paper uid="W03-0908">
  <Title>Towards Light Semantic Processing for Question Answering</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Summary and Future Work
</SectionTitle>
    <Paragraph position="0"> The paper presents a lightweight semantic processing technique for open-domain question answering. We propose a uniform semantic representation for questions and passages, derived from their functional structure. We also describe the unification framework which allows for flexible matching of query terms with retrieved passages.</Paragraph>
    <Paragraph position="1"> One characteristics of the current representation is that it is built from grammatical functions and does not utilize a canonical set of semantic roles and concepts. Our overall approach in JAVELIN was to start with the simplest form of meaning-based matching that could extend simple keyword-based approaches. Since it was possible to extract grammatical functions from unrestricted text fairly quickly (using KANTOO for questions and the Link Grammar parser for answer passages), this framework provides a reasonable first step. We intend to extend our representation and unification algorithm by incorporating the Lexical Conceptual Structure Database (Dorr, 2001), which will allow us to use semantic roles instead of grammatical relations as predicates in the representation. We also plan to enrich the representation with temporal expressions, incorporating the ideas presented in (Han, 2003).</Paragraph>
    <Paragraph position="2"> Another limitation of the current implementation is the limited scope of the similarity function. At present, the similarity function is based on relationships found in WordNet, and only relates words which belong to the same syntactic category. We plan to extend our similarity measure by using name lists, gazetteers and statistical cooccurrence in text corpora. A complete approach to word similarity will also require a suitable algorithm for reference resolution. Unrestricted text makes heavy use of various forms of co-reference, such as anaphora, definite description, etc. We intend to adapt the anaphora resolution algorithms used in KANTOO for this purpose, but a general solution to resolving definite reference (e.g., the use of &amp;quot;the organization&amp;quot; to refer to &amp;quot;Microsoft&amp;quot;) is a topic for ongoing research.</Paragraph>
  </Section>
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