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Probabilistic Part-of-Speech Tagging Using Decision Trees (1994)
| Citations: | 1005 - 9 self |
BibTeX
@MISC{Schmid94probabilisticpart-of-speech,
author = {Helmut Schmid},
title = {Probabilistic Part-of-Speech Tagging Using Decision Trees},
year = {1994}
}
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Abstract
In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method, a part-of-speech tagger (called TreeTagger) has been implemented which achieves 96.36 % accuracy on Penn-Treebank data which is better than that of a trigram tagger (96.06 %) on the same data.
Keyphrases
transition probability part-of-speech tagging sparse data english word form store decision tree avoids problem new probabilistic tagging method trigram tagger markov model introduction word form corpus-based nlp tagging method penn-treebank data statistical nlp automatic part finite verb part-of-speech tagger
