Document (#38122)

Author

Manning, C.D.

Title

Part-of-Speech Tagging from 97% to 100% : is it time for some linguistics?

Source

Computational Linguistics and Intelligent Text Processing, 12th International Conference, CICLing 2011, Proceedings, Part I. Ed.: Alexander Gelbukh

Imprint

Berlin : Springer

Year

2011

Pages

S.171-189

Series

Lecture notes in computer science; 6608

Abstract

I examine what would be necessary to move part-of-speech tagging performance from its current level of about 97.3% token accuracy (56% sentence accuracy) to close to 100% accuracy. I suggest that it must still be possible to greatly increase tagging performance and examine some useful improvements that have recently been made to the Stanford Part-of-Speech Tagger. However, an error analysis of some of the remaining errors suggests that there is limited further mileage to be had either from better machine learning or better features in a discriminative sequence classifier. The prospects for further gains from semisupervised learning also seem quite limited. Rather, I suggest and begin to demonstrate that the largest opportunity for further progress comes from improving the taxonomic basis of the linguistic resources from which taggers are trained. That is, from improved descriptive linguistics. However, I conclude by suggesting that there are also limits to this process. The status of some words may not be able to be adequately captured by assigning them to one of a small number of categories. While conventions can be used in such cases to improve tagging consistency, they lack a strong linguistic basis.

Content

Vgl.: http://nlp.stanford.edu/~manning/papers/CICLing2011-manning-tagging.pdf.

Theme

Computerlinguistik

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5. Losee, R.M.: Learning syntactic rules and tags with genetic algorithms for information retrieval and filtering : an empirical basis for grammatical rules (1996) 0.25

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