|Table of Contents|

Domain Expert Entity Extraction Method Based on Multi-FeatureBidirectional Gated Neural Network(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年01期
Page:
128-135
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Domain Expert Entity Extraction Method Based on Multi-FeatureBidirectional Gated Neural Network
Author(s):
Zhang Kewen; Li Xiang; Yan Yunyang; Zhu Quanyin; Ma Jialin
Faculty of Computer and Software Engineering,Huaiyin Institute of Technology,Huai’an 223005,China
Keywords:
named entity recognition; natural language processing; information extraction; multi-feature; boundary feature
PACS:
TP301.6
DOI:
10.3969/j.issn.1001-4616.2021.01.018
Abstract:
Named entity recognition is the basic task of natural language processing(NLP)and information extraction(IE). Traditional expert named entity recognition methods have problems,such as excessive reliance on artificial feature labeling and word segmentation effects,and the inability to recognize a large number of professional new words in the expert profile. This paper proposes a method based on multi-features bidirectional gated neural network structure combined with conditional random field model for the domain expert entity extraction. Firstly,train the entity extraction model by constructing a domain expert corpus. Secondly,use the Bert method to represent the word embedding,and perform feature analysis on the vocabulary structure elements of the professional field of the corpus and extract the boundary features. Thirdly,use the bidirectional gated neural network and attention mechanism to effectively obtain the long-distance dependence of specific words. Finally,combine the conditional random field model to achieve named entity recognition. The experimental comparison and analysis of five methods on the same data set show that the F1 value of the model is improved by more than 9.98% compared with BiLSTM-CRF and IDCNN-CRF.

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Last Update: 2021-03-15