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Proceedings Paper

Bidirectional LSTM-CRF models for keyword extraction in Chinese sport news
Author(s): Yiqi Jiang; Tongzhou Zhao; Yue Chai; Peidong Gao
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Paper Abstract

State-of-the-art methods of keyword extraction from news are based on traditional machine learning and their performances rely heavily on hand-crafted feature and domain-specific knowledge. In this paper, we propose a new character-based method for keyword extraction from Chinese sport news, which based bidirectional Long Short-Term Memory with Conditional Random Field (BILSTM-CRF). The experiments result shows that BILSTM-CRF can effectively improve the performance of keyword extraction in Chinese sport news.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300H (14 February 2020); doi: 10.1117/12.2538057
Show Author Affiliations
Yiqi Jiang, Wuhan Institute of Technology (China)
Tongzhou Zhao, Wuhan Institute of Technology (China)
Yue Chai, Wuhan Institute of Technology (China)
Peidong Gao, Wuhan Institute of Technology (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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