The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
중국어 명명 개체 인식은 중국어 자연어 처리 분야의 기본 기술입니다. 정보 추출, 지능형 질문 답변 및 지식 그래프에 광범위하게 채택됩니다. 그럼에도 불구하고 중국어의 다양성과 복잡성으로 인해 대부분의 중국어 NER 방법은 중국어 NER의 성능에 영향을 미치는 문자 세분성 의미를 충분히 포착하지 못합니다. 본 연구에서는 DSKE-Chinese NER: Dictionary Semantic Knowledge Enhancement 기반의 Chinese Named Entity Recognition을 제안한다. 우리는 문자 입도의 의미론적 정보를 문자의 벡터 공간으로 참신하게 통합하고 어텐션 메커니즘에 의해 의미론적 정보를 포함하는 벡터 표현을 획득합니다. 또한 비교 실험을 통해 시맨틱 레이어의 적절한 개수를 검증한다. Weibo, Resume 및 MSRA와 같은 공개 중국어 데이터 세트에 대한 실험은 모델이 문자 기반 LSTM 기준선을 능가하는 것으로 나타났습니다.
Tianbin WANG
Information Engineering University
Ruiyang HUANG
Information Engineering University
Nan HU
Songshan Laboratory
Huansha WANG
Information Engineering University
Guanghan CHU
Information Engineering University
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부
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
부
@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},}
부
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 -