[1]WANG Yicheng,WAN Fucheng,MA Ning.Chinese semantic role labeling with multi-level linguistic features[J].CAAI Transactions on Intelligent Systems,2020,15(1):107-113.[doi:10.11992/tis.201910012]
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Chinese semantic role labeling with multi-level linguistic features
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
107-113
Column:
学术论文—自然语言处理与理解
Public date:
2020-01-05
- Title:
- Chinese semantic role labeling with multi-level linguistic features
- Keywords:
- natural language processing; semantic role labeling; deep learning; Bi-LSTM; linguistic characteristics; post-processing layer; Max pooling
- CLC:
- TP391
- DOI:
- 10.11992/tis.201910012
- Abstract:
- With the rapid development of artificial intelligence and Chinese information processing technology, studies relating to natural language processing have reached the level of semantic understanding gradually, while Chinese Semantic Role Labeling is the key technology in the semantic understanding field. Traditional tagging methods depend heavily on the parsing degree of sentence syntax and semantics, so the accuracy of tagging is limited. Aiming at the above problems, this paper improves the basic model of Chinese Semantic Role Labeling based on Bi-LSTM. To solve the above problem, the Max pooling technology is combined in the post-processing stage of the model, and multi-level linguistic features such as lexical item and sentence pattern are integrated into the training to further improve the original annotation model. Through a number of experimental demonstrations, combined with linguistic assistant analysis, targeted improvement methods are proposed to improve the accuracy of model annotation. It is proved that the Bi-LSTM semantic role labeling model combined with Max pooling technology can improve the effect of model annotation by incorporating relevant linguistic features.
- References:
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1900-01-01