BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text

被引：0
|
作者：
Zhu, Ming [1 ]
Li, Huakang [1 ,2 ,3 ]
Sun, Xiaoyu [1 ]
Yang, Zhuo [4 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Peoples R China
[2] Lab Urban Land Resources Monitoring & Simulat She, Shenzhen 518034, Peoples R China
[3] Suzhou Privacy Informat Technol Co, Suzhou 215011, Peoples R China
[4] Guangdong Univ Technol, Sch Sch Comp, Guangzhou, Peoples R China
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE-RCAR 2020) | 2020年
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
POS attention model; Named entity recognition; Irregular short text; BI-LSTM-CRF; BLAC;
D O I：
10.1109/rcar49640.2020.9303256
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Irregular text refers to text with incomplete sequence information of the sentence or text that does not meet normal grammatical specifications, such as Weibo text. Existing named entity recognition algorithms recognize such text, the effect is poor due to the lack of context information. Because the attention mechanism has advantages in obtaining contextual information, we merge part-of-speech attention with the BI-LSTM-CRF model and propose a BLAC model. We tested on several public datasets and compared the results with the basic model BI-LSTM-CRF. The results show that the method we proposed has a certain improvement in the entity recognition of irregular short text.
引用
收藏
页码：56 / 61
页数：6
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