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Predicate-attention neural model for Chinese semantic role labeling

文献类型：期刊论文

作者Song, Heng1,2; Wang, Shi1; Liu, Yu1,2; Wang, Ya1,2
刊名COMPUTERS & ELECTRICAL ENGINEERING
出版日期2022-04-01
卷号99页码:12
关键词Chinese semantic role labeling Attention mechanism Argument identification Argument classification Semantic parsing
ISSN号0045-7906
DOI10.1016/j.compeleceng.2022.107741
英文摘要Semantic role labeling functions to convey the meaning of a sentence through forming a predicate-argument structure directed at the specific predicate. In recent years, end-toend semantic role labeling methods associated with the deep neural network have received significant attention in the field of computational linguistics. Moreover, end-to-end semantic role labeling methods have demonstrated a beneficial capacity to reduce the incompleteness caused by handcrafted features, which is an observed short-coming of traditional Chinese role labeling methods. However, the critical focus of sentences are frequently lost as a result of existing semantic role labeling structures attributing equal importance to every single word, instead of the overall concept denoted by particular terms. Hence, the performance and function ability of deep neural network models is reduced. In this paper, we introduce a specific attention mechanism based on the established predicate. This mechanism would automatically calculate the weighted contributions of each word, and the corresponding Part-of-Speech, in order to accurately represent the general fundamental ideas of the sentence. In addition, we extended the Bidirectional LSTM using two different semantic role constraint methods, to effectively utilize the dependency and constraint relationships among different semantic role tags, hereby further improving the performance of the whole neural Chinese semantic role labeling model. Experimental results demonstrate the efficacy of our proposed model through providing a baseline that allows for meaningful comparisons, inferring that both weighted contributions of the predicate, and semantic role constraints can help significantly refine the overall model function.
资助项目Interdisciplinary Cooperation Project of Beijing Science and Technology New Star Program, China[Z191100001119014]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000754537600007
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/18972]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Shi
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Song, Heng,Wang, Shi,Liu, Yu,et al. Predicate-attention neural model for Chinese semantic role labeling[J]. COMPUTERS & ELECTRICAL ENGINEERING,2022,99:12.
APA Song, Heng,Wang, Shi,Liu, Yu,&Wang, Ya.(2022).Predicate-attention neural model for Chinese semantic role labeling.COMPUTERS & ELECTRICAL ENGINEERING,99,12.
MLA Song, Heng,et al."Predicate-attention neural model for Chinese semantic role labeling".COMPUTERS & ELECTRICAL ENGINEERING 99(2022):12.

入库方式： OAI收割

来源：计算技术研究所

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