A Feature-Rich Vietnamese Named-Entity Recognition Model - ArXiv

Computer Science > Computation and Language arXiv:1803.04375 (cs) [Submitted on 12 Mar 2018] Title:A Feature-Rich Vietnamese Named-Entity Recognition Model Authors:Pham Quang Nhat Minh View a PDF of the paper titled A Feature-Rich Vietnamese Named-Entity Recognition Model, by Pham Quang Nhat Minh View PDF

Abstract:In this paper, we present a feature-based named-entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also explore the effects of word segmentation, PoS tagging, and chunking results of many popular Vietnamese NLP toolkits on the accuracy of the proposed feature-based NER model. Up to now, our work is the first work that systematically performs an extrinsic evaluation of basic Vietnamese NLP toolkits on the downstream NER task. Experimental results show that while automatically-generated word segmentation is useful, PoS and chunking information generated by Vietnamese NLP tools does not show their benefits for the proposed feature-based NER model.

Comments:	12 pages, pre-print version of CICLing 2018 paper
Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:1803.04375 [cs.CL]
(or arXiv:1803.04375v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.1803.04375 Focus to learn more arXiv-issued DOI via DataCite

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From: Quang Nhat Minh Pham Mr [view email] [v1] Mon, 12 Mar 2018 17:07:40 UTC (26 KB) Full-text links:

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