Intelligent Probabilistic Approaches to Natural Language
Papers from the 1992 Fall Symposium
Robert Goldman, Program Chair
Technical Report FS-92-04. Published by The AAAI Press, Menlo Park, California
This technical report is available in book format.
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Contents
Combining NLP and Statistical Techniques for Lexical Acquisition / 1
Roberto Basili, Maria Teresa Pazienza, and Paola Velardi
Tagging an Unfamiliar Text With Minimal Human Supervision / 10
Eric Brill and Mitch Marcus
Probabilistic Resolution of Anaphoric Reference / 17
John Burger and Dennis Conolly
Learning Probabilistic Dependency Grammars from Labeled Text / 25
Glenn Carroll and Eugene Charniak
Probabilistic Normalization and Unpacking of Packed Parse Forests for Unification-based Grammars / 33
John Carroll and Ted Briscoe
Using a Bayesian Framework to Identify the Referent of Definite Descriptions / 39
Sehyeong Cho and Anthony Maida
Applying Statistical Methods to Small Corpora: Benefiting from a Limited Domain / 47
David Fisher and Ellen Riloff
Work on Statistical Methods for Word Sense Disambiguation / 54
Bill Gale, Kenneth Church, and David Yarowsky
Finding Semantic Similarity in Raw Text: the Deese Antonym / 61
Gregory Grefenstette
Recursive Markov Chain as a Stochastic Grammar / 67
Young Han, C. Park, and Key-Sun Choi
Parsing a Probabilistic Dependency Grammar / 74
Don Hindle
Combining Syntactic Knowledge and Visual Text Recognition: A Hidden Markov Model for Part of Speech Tagging in a Work Recognition Algorithm / 77
Jonathan Hull
Virtual Machine Translation / 84
Daniel Jones
Grammatical Trigrams: A Probabilistic Model of Link Grammar / 89
John Lafferty, Daniel Sleator, and Davy Temperley
Statistically Guided Work Sense Disambiguation / 98
Elizabeth Liddy and Woojin Paik
Distributional Similarity, Phase Transitions and Hierarchical Clustering / 108
Fernando Pereira and Naftali Tishby
Context Space / 113
Hinrich Schutze
Combining Statistical and Syntactic Methods in Recognizing Handwritten Sentences / 121
Rohini Srihari and Charlotte Baltus
Discourse versus Probability in the Theory of Natural Language Interpretation / 128
Bob Wilensky
A Nonstationary Hidden Markov Model with a Hard Capture of Observations: Application to the Problem of Morphological Ambiguities / 136
Djamel Bouchaffra and Jacques Rouault
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