Common sense inference in a focus-guided anaphor resolver

This paper describes an investigation of the feasibility of resolving anaphors in natural language texts by means of a ‘shallow processing’ approach which exploits knowledge of syntax, semantics and local focussing as heavily as possible; it does not rely on the presence of large amounts of world or domain knowledge, which are notoriously hard to process accurately. The ideas reported are implemented in a program called SPAR (Shallow Processing Anaphor Resolver), which resolves anaphoric ambiguities in simple English stories and generates sentence-by-sentence paraphrases that show what interpretations have been selected. To resolve anaphors, SPAR combines and develops several existing techniques, most notably Sidner's theory of local focussing 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 processing 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. In the absence of large quantities of world knowledge, successful anaphor resolution is seen to depend on the coordination of predictions made by system components exploiting various knowledge sources. Such coordination normally allows anaphors to be resolved correctly even when no single source of predictions is sufficient on its own