Weak Semi-Markov CRFs for Noun Phrase Chunking in Informal Text

Published in NAACL, 2016

Recommended citation: Muis, A. O., & Lu, W. (2016). Weak Semi-Markov CRFs for Noun Phrase Chunking in Informal Text. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 714–719). Stroudsburg, PA, USA: Association for Computational Linguistics. http://www.aclweb.org/anthology/N16-1085v2 https://arxiv.org/abs/1810.08567

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This paper introduces a new annotated corpus based on an existing informal text corpus: the NUS SMS Corpus (Chen and Kan, 2013). The new corpus includes 76,490 noun phrases from 26,500SMSmessages, annotated by university students. We then explored several graphical models, including a novel variant of the semi-Markov conditional random fields (semi-CRF) for the task of noun phrase chunking. We demonstrated through empirical evaluations on the new dataset that the new variant yielded similar accuracy but ran in significantly lower running time compared to the conventional semi-CRF.

Recommended citation: Muis, A. O., & Lu, W. (2016). Weak Semi-Markov CRFs for Noun Phrase Chunking in Informal Text. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 714–719). Stroudsburg, PA, USA: Association for Computational Linguistics. http://www.aclweb.org/anthology/N16-1085v2