[1]ZHU Yanhui,LI Fei,JI Xiangbing,et al.Domain-named entity recognition based on feedback K-nearest semantic transfer learning[J].CAAI Transactions on Intelligent Systems,2019,14(4):820-830.[doi:10.11992/tis.201804013]
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Domain-named entity recognition based on feedback K-nearest semantic transfer learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 820-830 Column: 学术论文—自然语言处理与理解 Public date: 2019-07-02
Title:
Domain-named entity recognition based on feedback K-nearest semantic transfer learning
Author(s):
ZHU Yanhui1; 2;  LI Fei1; 2;  JI Xiangbing1; 2;  ZENG Zhigao1; 2;  XU Xiao1; 2
1. School of Computer, Hu’nan University of Technology, Zhuzhou 412008, China;
2. Hu’nan Key Laboratory of Intelligent Information Perception and Processing Technology, Zhuzhou 412008, China
Keywords:
domain-named entity recognition; feedback K-nearest neighbor; semantic transfer learning; deep learning; CNN; Doc2Vec; Mahalanobis distance; packaging field; medical field
CLC:
TP391
DOI:
10.11992/tis.201804013
Abstract:
Domain-named entity recognition is an important foundation in constructing domain knowledge maps. In view of the scarcity of such recognition, this paper constructs a BiLSTM-CNN-CRFs network model based on deep learning as well as proposes a domain-named entity recognition method based on feedback K-nearest-neighbor semantic transfer learning. First, the corpus of the professional field and the general field were trained to obtain the corpus document vector, and the semantic similarity between the corpus of a domain and the common corpus was calculated using the Mahalanobis distance calculation. For each specialized domain sample, K common domain samples with the most similar semantics were taken for semantic transfer learning, and several transfer corpus sets were constructed. Then, the BiLSTM-CNN-CRFs network model was used to identify domain-named entities in N migration corpuses and evaluate and feedforward the recognition results. An appropriate K value was selected as the best threshold for semantic transfer learning according to the feedback results. The packaging and medical fields were taken as examples for experimental verification. The results showed that the method proposed in this paper has a good recognition effect and can effectively solve the problem of lack of corpus in the field of specialization.
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