Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging

Apostolos Kemos, Heike Adel, Hinrich Schütze


Abstract
Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. However, they often still rely on correct token boundaries. In this paper, we propose to eliminate the need for tokenizers with an end-to-end character-level semi-Markov conditional random field. It uses neural networks for its character and segment representations. We demonstrate its effectiveness in multilingual settings and when token boundaries are noisy: It matches state-of-the-art part-of-speech taggers for various languages and significantly outperforms them on a noisy English version of a benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.
Anthology ID:
N19-1280
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2736–2743
Language:
URL:
https://www.aclweb.org/anthology/N19-1280
DOI:
10.18653/v1/N19-1280
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1280.pdf