A finite and real-time processor for natural language
Communications of the ACM
Constraint-based grammar formalisms: parsing and type inference for natural and computer languages
Constraint-based grammar formalisms: parsing and type inference for natural and computer languages
An efficient context-free parsing algorithm
Communications of the ACM
Generalized L.R. Parsing
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
The interface between phrasal and functional constraints
Computational Linguistics
Using restriction to extend parsing algorithms for complex-feature-based formalisms
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Relating complexity to practical performance in parsing with wide-coverage unification grammars
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
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In this paper, we present an efficient algorithm for parsing natural language using unification grammars. The algorithm is an extension of left-corner parsing, a bottom-up algorithm which utilizes top-down expectations. The extension exploits unification grammar's uniform representation of syntactic, semantic, and domain knowledge, by incorporating all types of grammatical knowledge into parser expectations. In particular, we extend the notion of the reochcsbility table, which provides information as to whether or not a top-down expectation can be realized by a potential subconstituent, by including all types of grammatical information in table entries, rather than just phrase structure information. While our algorithm's worstcase computational complexity is no better than that of many other algorithms, we present empirical testing in which average-case linear time performance is achieved. Our testing indicates this to be much improved average-case performance over previous leftcorner techniques.