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An ongoing debate in text understanding efforts centers on the use of pattern-matching techniques, which some have characterized as "designed to ignore as much text as possible," versus approaches which primarily employ rules that are domain-independent and linguistically-motivated. For instance, in the message-processing community, there has been a noticeable pulling back from large-coverage grammars to the point where, in some systems, traditional models of syntax and semantics have been completely replaced by domain-specific finite-state approximations.In this paper we report on a hybrid approach which uses such domain-specific patterns as a supplement to domain-independent grammar rules, domain-independent semantic rules, and automatically hypothesized domain-specific semantic rules. The surprising result, as measured on TIPSTER test data, is that domain-specific pattern matching improved performance, but only slightly, over more general linguistically-motivated techniques.

Pattern matching in a linguistically-motivated text understanding system