IEICE TRANSACTIONS on Information
Chinese Named Entity Recognition Method Based on Dictionary Semantic Knowledge Enhancement
Summary :
Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.1010-1017
- Publication Date
- 2023/05/01
- Publicized
- 2023/02/15
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7168
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Tianbin WANG
Information Engineering University
Ruiyang HUANG
Information Engineering University
Nan HU
Songshan Laboratory
Huansha WANG
Information Engineering University
Guanghan CHU
Information Engineering University
Keyword
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Tianbin WANG, Ruiyang HUANG, Nan HU, Huansha WANG, Guanghan CHU, "Chinese Named Entity Recognition Method Based on Dictionary Semantic Knowledge Enhancement" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 1010-1017, May 2023, doi: 10.1587/transinf.2022EDP7168.
Abstract: Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7168/_p
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@ARTICLE{e106-d_5_1010,
author={Tianbin WANG, Ruiyang HUANG, Nan HU, Huansha WANG, Guanghan CHU, },
journal={IEICE TRANSACTIONS on Information},
title={Chinese Named Entity Recognition Method Based on Dictionary Semantic Knowledge Enhancement},
year={2023},
volume={E106-D},
number={5},
pages={1010-1017},
abstract={Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.},
keywords={},
doi={10.1587/transinf.2022EDP7168},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Chinese Named Entity Recognition Method Based on Dictionary Semantic Knowledge Enhancement
T2 - IEICE TRANSACTIONS on Information
SP - 1010
EP - 1017
AU - Tianbin WANG
AU - Ruiyang HUANG
AU - Nan HU
AU - Huansha WANG
AU - Guanghan CHU
PY - 2023
DO - 10.1587/transinf.2022EDP7168
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.
ER -
English
