tailieunhanh - Báo cáo khoa học: "Sentence Disambiguation by a Shift-Reduce Parsing Technique"

Native speakers of English show definite and consistent preferences for certain readings of syntactically ambiguous sentences. A user of a natural-language-processing system would naturally expect it to reflect the same preferences. Thus, such systems must model in some way the linguistic performance as well as the linguistic competence of the native speaker. We have developed a parsing algorithm--a variant of the LALR(I} algorithm--that models the preference behavior of native speakers for a range of syntactic preference phenomena reported in the psycholinguistic literature, including the recent data on lexical preferences. . | Sentence Disambiguation by a Shift-Reduce Parsing Technique Stuart M. Shieber Artificial Intelligence Center SRI International 333 Ravenswood Avenue Menlo Park CA 94025 Abstract Native speakers of English show definite and consistent preferences for certain readings of syntactically ambiguous sentences. A user of a natural-language-processing system would naturally expect it to reflect the same preferences. Thus such systems must model in some way the linguistic performance as well as the linguistic competence of the native speaker. We have developed a parsing algorithm a variant of the LALR l shift-reduce algorithm that models the preference behavior of native speakers for a range of syntactic preference phenomena reported in the psycholinguistic literature including the recent data on lexical preferences. The algorithm yields the preferred parse deterministically without building multiple parse trees and choosing among them. As a side effect it displays appropriate behavior in processing the much discussed garden-path sentences. The parsing algorithm has been implemented and has confirmed the feasibility of our approach to the modeling of these phenomena. 1. Introduction For natural language processing systems to be useful they must assign the same interpretation to a given sentence that a native speaker would since that is precisely the behavior users will expect. Consider for example the case of ambiguous sentences. Native speakers of English show definite and consistent preferences for certain readings of syntactically ambiguous sentences Kimball 1973 Frailer and Fodor 1978 Ford et al. 1982 . A user of a natural-language-processing system would naturally expect it to reflect the same preferences. Thus such systems must model in some way the linguistic performance as well as the linguistic competence of the native speaker. This idea is certainly not new in the artificial-intelligence literat ure. The pioneering work of Marcus Marcus 1980 is perhaps the best .

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