research-article

An Improved Chinese Named Entity Recognition Method with TB-LSTM-CRF

Authors:

Jiazheng Li,

Tao Wang,

Weiwen ZhangAuthors Info & Claims

SSPS '20: Proceedings of the 2020 2nd Symposium on Signal Processing Systems

Pages 96 - 100

https://doi.org/10.1145/3421515.3421534

Published: 17 December 2020 Publication History

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Abstract

Owing to the lack of natural delimiters, Chinese named entity recognition (NER) is more challenging than it in English. While Chinese word segmentation (CWS) is generally regarded as key and open problem for Chinese NER, its accuracy is critical for the downstream models trainings and it also often suffers from out-of-vocabulary (OOV). In this paper, we propose an improved Chinese NER model called TB-LSTM-CRF, which introduces a Transformer Block on top of LSTM-CRF. The proposed model with Transformer Block exploits the self-attention mechanism to capture the information from adjacent characters and sentence contexts. It is more practical with using small-size character embeddings. Compared with the baseline using LSTM-CRF, experiment results show our method TB-LSTM-CRF is competitive without the need of any external resources, for instance other dictionaries.

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https://doi.org/10.1109/ICETCI55101.2022.9832288

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SSPS '20: Proceedings of the 2020 2nd Symposium on Signal Processing Systems

July 2020

125 pages

ISBN:9781450388627

DOI:10.1145/3421515

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 December 2020

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SSPS 2020: 2020 2nd Symposium on Signal Processing Systems

July 11 - 13, 2020

Guangdong, China

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Xie XHuang MLiu YAn Q(2023)Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term MemoryMachines10.3390/machines1101009411:1(94)Online publication date: 11-Jan-2023
https://doi.org/10.3390/machines11010094
Wang WWang BLiu YLuo CWang L(2022)Research on Knowledge Graph Construction of Early Stage Auxiliary Decision-making System for Power Grid Planning Projects2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI)10.1109/ICETCI55101.2022.9832288(412-416)Online publication date: 27-May-2022
https://doi.org/10.1109/ICETCI55101.2022.9832288

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Research on Chinese Named Entity Recognition in the Marine Field

Chinese Named Entity Recognition with CRFs: Two Levels

Five-Stroke Based CNN-BiRNN-CRF Network for Chinese Named Entity Recognition

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