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Interpreting anaphors in natural language texts (Ellis Horwood series in artificial intelligence)

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Perhaps the major obstacle to the development of computer programs capable of the sophisticated processing of natural language is the problem of representing and using the large and varied quantities of world or domain knowledge that are, in general, required. This book describes an attempt to circumvent this obstacle for one aspect of the language processing problem - that of interpreting anaphors (pronouns and other abbreviated expressions) in texts - by adopting a "shallow processing" approach. In this approach, linguistic knowledge, about syntax, semantics and local focusing, is exploited as heavily as possible, in order to minimise reliance on world knowledge.

The ideas reported are implemented in a program called SPAR (Shallow Processing Anaphor Resolver), which resolves anaphoric and other linguistic ambiguities in simple English stories and generates sentence-by-sentence paraphrases that show what interpretations have been selected. Input to SPAR takes the form of semantic structures derived automatically from single sentences. These structures are integrated into a network-style text representation as processing proceeds. To achieve anaphor resolution, SPAR combines and devel- ops several existing techniques, most notably Sidner's theory of local focusing and Wilks' "preference semantics" theory of semantics and common sense inference.

Consideration of the need to resolve several anaphors in the same sentence results in Sidner's framework being modified and extended to allow focus-based processing to interact more flexibly with proces- sing based on other types of knowledge. Wilks' treatment of common sense inference is extended to incorporate a wider range of types of inference without jeopardizing its uniformity and simplicity. Further, his primitive-based formalism for word sense meanings is developed in the interests of economy, accuracy and ease of use.
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