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Algorithms For Bigram And Trigram Word Clustering (1995)
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| Venue: | Speech Communication |
| Citations: | 67 - 0 self |
BibTeX
@INPROCEEDINGS{Martin95algorithmsfor,
author = {Sven Martin and Jörg Liermann and Hermann Ney},
title = {Algorithms For Bigram And Trigram Word Clustering},
booktitle = {Speech Communication},
year = {1995},
pages = {1253--1256}
}
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Abstract
. This paper presents and analyzes improved algorithms for clustering bigram and trigram word equivalence classes, and their respective results: 1) We give a detailed time complexity analysis of bigram clustering algorithms. 2) We present an improved implementation of bigram clustering so that large corpora (38 million words and more) can be clustered within a small number of days or even hours. 3) We extend the clustering approach from bigrams to trigrams. 4) We present experimental results on a 38 million word training corpus. 1. INTRODUCTION Word equivalence classes are a method for improving undertrained word M--gram language models [1], [2], [4]. Words are grouped into classes, and each word belongs to only one such class. Thus, if a word pair is not seen in training, it is quite likely that the corresponding class pair is seen. For bigram and trigram class models, we have the equations p(wn jwn\Gamma1 ) = p0(wn jG(wn)) (1) \Deltap 1(G(wn)jG(wn\Gamma1)) p(wn jwn\Gamma2 ; wn\Gam...
Keyphrases
trigram word clustering detailed time complexity analysis word training corpus even hour trigram word equivalence class wn jwn gamma2 respective result corresponding class pair undertrained word gram language model wn gamma1 introduction word equivalence class present experimental result word pair trigram class model bigram clustering algorithm wn jg large corpus small number improved implementation bigram clustering wn jwn gamma1
