Unsupervised Part-of-Speech Acquisition for Resource-Scarce Languages

Sajib Dasgupta and Vincent Ng.
Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 218-227, 2007.

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Abstract

This paper proposes a new bootstrapping approach to unsupervised part-of-speech induction. In contrast to previous bootstrapping algorithms developed for this problem, our approach aims to improve the quality of the seed clusters by employing seed words that are both distributionally and morphologically reliable. In particular, we present a novel method for combining morphological and distributional information for seed selection. Experimental results demonstrate that our approach works well for English and Bengali, thus providing suggestive evidence that it is applicable to both morphologically impoverished languages and highly inflectional languages.

Dataset

The Bengali dataset used in this paper is available from here.

BibTeX entry

@InProceedings{Dasgupta+Ng:07b,
  author = {Sajib Dasgupta and Vincent Ng},
  title = {Unsupervised Part-of-Speech Acquisition for Resource-Scarce Languages},
  booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
  pages = {218--227},
  year = 2007
}

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