TY - GEN
T1 - Research on semantic role labeling method
AU - Jiang, Bo
AU - Lan, Yuqing
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Deep neural networks
KW - Semantic analysis
KW - Semantic role labeling
UR - https://www.scopus.com/pages/publications/85060675707
U2 - 10.1007/978-3-030-06161-6_25
DO - 10.1007/978-3-030-06161-6_25
M3 - 会议稿件
AN - SCOPUS:85060675707
SN - 9783030061609
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 252
EP - 258
BT - Communications and Networking - 13th EAI International Conference, ChinaCom 2018, Proceedings
A2 - Cheng, Dai
A2 - Jinfeng, Lai
A2 - Liu, Xingang
PB - Springer Verlag
T2 - 13th EAI International Conference on Communications and Networking in China, ChinaCom 2018
Y2 - 23 October 2018 through 25 October 2018
ER -