[1]ZHOU Hao,WANG Li.Chinese opinion target extraction based on fusion of semantic and syntactic information[J].CAAI Transactions on Intelligent Systems,2019,14(1):171-178.[doi:10.11992/tis.201809029]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 1
Page number:
171-178
Column:
学术论文—自然语言处理与理解
Public date:
2019-01-05
- Title:
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Chinese opinion target extraction based on fusion of semantic and syntactic information
- Author(s):
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ZHOU Hao1; WANG Li2
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1. College of Information and Computer Science, Taiyuan University of Technology, Jinzhong 030600, China;
2. College of Big Data, Taiyuan University of Technology, Jinzhong 030600, China
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- Keywords:
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Chinese opinion target; semantic; syntactic; sequence labeling; bidirectional long short-term memory; conditional random field; extraction model
- CLC:
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TP391
- DOI:
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10.11992/tis.201809029
- Abstract:
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The regular method of Chinese opinion target extraction has poor accuracy, and it ignores Chinese semantics and syntactic information. Therefore, a Chinese opinion target extraction model that combines semantic and syntactic information has been proposed. On the basis of the original word vector, the model strengthens the semantic features by optimizing the character meaning strategy, so as to make up for the internal information between the ignored characters and words, and through part-of-speech sequence annotation, the word-of-speech information of the sentence is characterized, and it represents the input syntactic information in depth. Through the bidirectional long short-term memory and the conditional random field, the deviation of the labeled label is avoided, improving the extraction accuracy. The model was validated on the BDCI2017 dataset. When compared with a unincorporated semantics and grammar extraction model, the accuracy of Chinese keyword and sentiment extraction increased by 2.1% and 1.68%, respectively. The accuracy of joint extraction was 77.16%, indicating a good effect on Chinese opinion target extraction.