Research Article
Research on Semantic Role Labeling Method
@INPROCEEDINGS{10.1007/978-3-030-06161-6_25, author={Bo Jiang and Yuqing Lan}, title={Research on Semantic Role Labeling Method}, proceedings={Communications and Networking. 13th EAI International Conference, ChinaCom 2018, Chengdu, China, October 23-25, 2018, Proceedings}, proceedings_a={CHINACOM}, year={2019}, month={1}, keywords={Semantic role labeling Semantic analysis Deep neural networks}, doi={10.1007/978-3-030-06161-6_25} }- Bo Jiang
Yuqing Lan
Year: 2019
Research on Semantic Role Labeling Method
CHINACOM
Springer
DOI: 10.1007/978-3-030-06161-6_25
Abstract
Semantic role labeling task is a way of shallow semantic analysis. Its research results are of great significance for promoting Machine Translation [1], Question Answering [2], Human Robot Interaction [3] and other application systems. The goal of semantic role labeling is to recover the predicate-argument structure of a sentence, based on the sentences entered and the predicates specified in the sentence. Then mark the relationship between the predicate and the argument, such as time, place, the agent, the victim, and so on. This paper introduces the main research directions of semantic role labeling and the research status at home and abroad in recent years. And summarized a large number of research results based on statistical machine learning and deep neural networks. The main purpose is to analyze the method of semantic role labeling and its current status. Summarize the development trend of the future semantic role labeling.