Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo khoa học: "Approximating Context-Free Grammars with a Finite-State Calculus"

Although adequate models of human language for syntactic analysis and semantic interpretation are of at least contextfree complexity, for applications such as speech processing in which speed is important finite-state models are often preferred. These requirements may be reconciled by using the more complex grammar to automatically derive a finite-state approximation which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage of processing. | Approximating Context-Free Grammars with a Finite-State Calculus Edmund GRIMLEY EVANS Computer Laboratory University of Cambridge Cambridge CB2 3QG GB Edmund.Grimley-Evans@cl.cam.ac.uk Abstract Although adequate models of human language for syntactic analysis and semantic interpretation are of at least context-free complexity for applications such as speech processing in which speed is important finite-state models are often preferred. These requirements may be reconciled by using the more complex grammar to automatically derive a finite-state approximation which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage of processing. A method is presented here for calculating such finite-state approximations from context-free grammars. It is essentially different from the algorithm introduced by Pereira and Wright 1991 1996 is faster in some cases and has the advantage of being open-ended and adaptable. 1 Finite-state approximations Adequate models of human language for syntactic analysis and semantic interpretation are typically of context-free complexity or beyond. Indeed Prolog-style definite clause grammars DCGs and formalisms such as PATR with feature-structures and unification have the power of Turing machines to recognise arbitrary recursively enumerable sets. Since recognition and analysis using such models may be computationally expensive for applications such as speech processing in which speed is important finite-state models are often preferred. When natural language processing and speech recognition are integrated into a single system one may have the situation of a finite-state language model being used to guide speech recognition while a unification-based formalism is used for subsequent processing of the same sentences. Rather than write these two grammars separately which is likely to lead to problems in maintaining consistency it would be preferable to derive the finite-state grammar automatically .