TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition |
| 영문제목(English Title) |
MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition |
| 저자(Author) |
Jingxin Liu
Jieren Cheng
Xin Peng
Zeli Zhao
Xiangyan Tang
Victor S. Sheng
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| 원문수록처(Citation) |
VOL 16 NO. 06 PP. 1833 ~ 1848 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.
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| 키워드(Keyword) |
Chinese Named Entity Recognition
Multi-view Feature Fusion Embedding
Natural Language Processing
Semantic Feature Fusion
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| 파일첨부 |
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