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Abstract: This technical report presents a probabilistic model of English grammar that is based upon "grammatical bigrams", i.e., syntactic relationships between pairs of words. Because of its simplicity, the grammatical bigram model admits cubic-time parsing and unsupervised learning algorithms, which are described in detail. (Update)
Context of citations to this paper: More
...algorithm for training the model. Details regarding the parsing and learning algorithms can be found in a companion technical report [4]. Dependency Grammar Formalism. 1 The primary unit of syntactic structure in dependency grammars is the dependency relationship, or link a...
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BibTeX entry: (Update)
Mark A. Paskin. Cubic-time parsing and learning algorithms for grammatical bigram models. Technical Report CSD-01-1148, University of California, Berkeley, 2001. http://citeseer.ist.psu.edu/paskin01cubictime.html More
@misc{ paskin01cubictime,
author = "M. Paskin",
title = "Cubic-time parsing and learning algorithms for grammatical bigram models",
text = "Mark A. Paskin. Cubic-time parsing and learning algorithms for grammatical
bigram models. Technical Report CSD-01-1148, University of California, Berkeley,
2001.",
year = "2001",
url = "citeseer.ist.psu.edu/paskin01cubictime.html" }
Citations (may not include all citations):131 The estimation of stochastic context-free grammars using the.. (context) - Lari, Young - 1990
96 Head-driven Statistical Models for Natural Language Parsing - Collins - 1999
95 The zero-frequency problem: Estimating the probabilities of .. (context) - Witten, Bell - 1991
58 English Word Grammar - Hudson - 1990
45 Estimation of probabilities from sparse data for the languag.. - Katz - 1987
15 An empirical comparison of probability models for dependency.. - Eisner - 1996
15 Discovery of Linguistic Relations Using Lexical Attraction - Yuret - 1998
12 Bilexical grammars and their cubic-time parsing algorithms - Eisner - 2000
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