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A Multi-domain Named Entity Recognition Method Based on Part-of-Speech Attention Mechanism

  • Shun Zhang
  • Ying Sheng
  • Jiangfan Gao
  • Jianhui ChenEmail author
  • Jiajin Huang
  • Shaofu Lin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

Named entity recognition is an important and basic work in text mining. To overcome the shortcomings of existing multi-domain named entity recognition methods, a multi-domain named entity recognition method based on the part-of-speech attention mechanism, called BiLSTM-ATTENTION-CRF, was proposed in this paper. The domain dictionary was constructed to represent multi-domain semantic information and the BiLSTM network was used to capture the grammatical and syntactic features, as well as multi-domain semantic features in context information. A part-of-speech attention mechanism was designed to obtain the contribution weight of part-of-speech for entity recognition. Finally, a group of experiments were performed on the multi-domain dataset to compare various fusion strategies of multi-level entity information. The experimental results show that BiLSTM-ATTENTION-CRF has a high precision and recall rate, and can effectively recognizes the multi-domain named entities.

Keywords

Multi-domain entity recognition Attention mechanism BiLSTM CRF 

Notes

Acknowledgment

The work received support from Science and Technology Project of Beijing Municipal Commission of Education (No. KM201710005026), National Basic Research Program of China (No. 2014CB744600), Open Foundation of Beijing Key Laboratory of MRI and Brain Informatics, Open Foundation of Beijing Key Laboratory of Multimedia and Intelligent Software (Beijing University of Technology).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shun Zhang
    • 1
  • Ying Sheng
    • 1
  • Jiangfan Gao
    • 1
  • Jianhui Chen
    • 1
    • 3
    Email author
  • Jiajin Huang
    • 1
    • 3
  • Shaofu Lin
    • 1
    • 2
  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Institute of Smart CityBeijing University of TechnologyBeijingChina
  3. 3.Beijing Key Laboratory of MRI and Brain InformaticsBeijingChina

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