Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF

Yan Shao, Christian Hardmeier, Jörg Tiedemann, Joakim Nivre

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.
Original languageEnglish
Title of host publicationProceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Place of PublicationTaipei, Taiwan
PublisherAsian Federation of Natural Language Processing
Pages173-183
Number of pages11
ISBN (Electronic)978-1-948087-00-1
Publication statusPublished - 1 Dec 2017
EventThe 8th International Joint Conference on Natural Language Processing - Taipei, Taiwan, Province of China
Duration: 27 Nov 20171 Dec 2017
http://ijcnlp2017.org/site/page.aspx?pid=901&sid=1133&lang=en

Conference

ConferenceThe 8th International Joint Conference on Natural Language Processing
Abbreviated titleIJCNLP 2017
CountryTaiwan, Province of China
CityTaipei
Period27/11/171/12/17
Internet address

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