John Lafferty

Also published as: John D. Lafferty, J. Lafferty, John Lafrerty


2007

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Computationally Efficient M-Estimation of Log-Linear Structure Models
Noah A. Smith | Douglas L. Vail | John D. Lafferty
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

1997

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Text Segmentation Using Exponential Models
Doug Beeferman | Adam Berger | John Lafferty
Second Conference on Empirical Methods in Natural Language Processing

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A Model of Lexical Attraction and Repulsion
Doug Beeferman | Adam Berger | John Lafferty
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

1995

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A Robust Parsing Algorithm for Link Grammars
Dennis Grinberg | John Lafferty | Daniel Sleator
Proceedings of the Fourth International Workshop on Parsing Technologies

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, 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 Wide Web for experimentation. It can be reached at URL http://www.cs.cmu.edu/afs/es.emu.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.

1994

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The Candide System for Machine Translation
Adam L. Berger | Peter F. Brown | Stephen A. Della Pietra | Vincent J. Della Pietra | John R. Gillett | John D. Lafferty | Robert L. Mercer | Harry Printz | Lubos Ures
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Decision Tree Parsing using a Hidden Derivation Model
F. Jelinek | J. Lafferty | D. Magerman | R. Mercer | A. Ratnaparkhi | S. Roukos
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
Ezra Black | Fred Jelinek | John Lafrerty | David M. Magerman | Robert Mercer | Salim Roukos
31st Annual Meeting of the Association for Computational Linguistics

1992

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Decision Tree Models Applied to the Labeling of Text with Parts-of-Speech
Ezra Black | Fred Jelinek | John Lafferty | Robert Mercer | Salim Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
Ezra Black | Fred Jelinek | John Lafferty | David M. Magerman | Robert Mercer | Salim Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Development and Evaluation of a Broad-Coverage Probabilistic Grammar of English-Language Computer Manuals
Ezra Black | John Lafferty | Salim Roukos
30th Annual Meeting of the Association for Computational Linguistics

1991

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Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Grammars
Frederick Jelinek | John D. Lafferty
Computational Linguistics, Volume 17, Number 3, September 1991

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Erratum to: A Statistical Approach to Machine Translation
Peter F. Brown | Stephen A. Della Pietra | Fredrick Jelinek | Robert L. Mercer | John Cocke | Vincent J. Della Pietra | John D. Lafferty | Paul S. Roossin
Computational Linguistics, Volume 17, Number 3, September 1991

1990

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A Statistical Approach to Machine Translation
Peter F. Brown | John Cocke | Stephen A. Della Pietra | Vincent J. Della Pietra | Fredrick Jelinek | John D. Lafferty | Robert L. Mercer | Paul S. Roossin
Computational Linguistics, Volume 16, Number 2, June 1990