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

Ming Zhu, Huakang Li*, Xiaoyu Sun, Zhuo Yang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages56-61
Number of pages6
ISBN (Electronic)9781728172927
DOIs
Publication statusPublished - 28 Sept 2020
Externally publishedYes
Event2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020 - Virtual, Asahikawa, Hokkaido, Japan
Duration: 28 Sept 202029 Sept 2020

Publication series

Name2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020

Conference

Conference2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
Country/TerritoryJapan
CityVirtual, Asahikawa, Hokkaido
Period28/09/2029/09/20

Keywords

  • BI-LSTM-CRF
  • BLAC
  • Irregular short text
  • Named entity recognition
  • POS attention model

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