Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Cubic-time Parsing and Learning Algorithms for Grammatical Bigram Models

Mark A. Paskin

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-01-1148
June 2001

http://www.eecs.berkeley.edu/Pubs/TechRpts/2001/CSD-01-1148.pdf

This paper 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.


BibTeX citation:

@techreport{Paskin:CSD-01-1148,
    Author = {Paskin, Mark A.},
    Title = {Cubic-time Parsing and Learning Algorithms for Grammatical Bigram Models},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2001},
    Month = {Jun},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2001/5544.html},
    Number = {UCB/CSD-01-1148},
    Abstract = {This paper 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.}
}

EndNote citation:

%0 Report
%A Paskin, Mark A.
%T Cubic-time Parsing and Learning Algorithms for Grammatical Bigram Models
%I EECS Department, University of California, Berkeley
%D 2001
%@ UCB/CSD-01-1148
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2001/5544.html
%F Paskin:CSD-01-1148