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A robust parsing algorithm for link grammars

著者: Dennis Grinberg; John D Lafferty; Daniel D Sleator
出版商: Pittsburgh, Pa. : School of Computer Science, Carnegie Mellon University, [1995]
丛书: Research paper (Carnegie Mellon University. School of Computer Science), CMU-CS-95-125.
版本/格式:   图书 : 英语
数据库:WorldCat
提要:
Abstract: "In this paper we present a robust parsing algorithm based on the link grammar formalism for parsing natural languages. Our algorithm is a natural extension of the original dynamic programming recognition algorithm which recursively counts the number of linkages between two words in the input sentence. The modified algorithm uses the notion of a null link in order to allow a connection between any pair of  再读一些...
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所有的著者/提供者: Dennis Grinberg; John D Lafferty; Daniel D Sleator
OCLC号码: 33830907
注意: "August 1995."
描述: 17 pages : illustrations ; 28 cm.
丛书名: Research paper (Carnegie Mellon University. School of Computer Science), CMU-CS-95-125.
责任: Dennis Grinberg, John Lafferty, and Daniel Sleator.

摘要:

Abstract: "In this paper we present a robust parsing algorithm based on the link grammar formalism for parsing natural languages. Our algorithm is a natural extension of the original dynamic programming recognition algorithm which recursively counts the number of linkages between two words in the input sentence. The modified algorithm uses the notion of a null link in order to allow a connection between any pair of adjacent words, regardless of their dictionary definitions. The algorithm proceeds by making three dynamic programming passes. In the first pass, the input is parsed using the original algorithm which enforces the constraints on links to ensure grammaticality. In the second pass, the total cost of each substring of words is computed, where cost is determined by the number of null links necessary to parse the substring. The final pass counts the total number of parses with minimal cost. All of the original pruning techniques have natural counterparts in the robust algorithm. When used together with memoization [sic], these techniques enable the algorithm to run efficiently with cubic worst-case complexity. We have implemented these ideas and tested them by parsing the Switchboard corpus of conversational English. This corpus is comprised of approximately three million words of text, corresponding to more than 150 hours of transcribed speech collected from telephone conversations restricted to 70 different topics. Although only a small fraction of the sentences in this corpus are 'grammatical' by standard criteria, the robust link grammar parser is able to extract relevant structure for a large portion of the sentences. We present the results of our experiments using this system, including the analyses of selected and random sentences from the corpus. We placed a version of the robust parser on the Word [sic] Wide Web for experimentation. It can be reached at URL http://www.cs.cmu.edu/afs/cs.cmu.edu/project/link/www/robust.html. In this version there are some limitations such as the maximum length of a sentence in words and the maximum amount of memory the parser can use."

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