Open AccessResearch of semantic role labeling based on long short-term memory neural networks
Author(s) -
Xuheng Liang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012005
Subject(s) - computer science , task (project management) , artificial intelligence , natural language processing , artificial neural network , semantic role labeling , feature engineering , term (time) , semantic memory , feature (linguistics) , recurrent neural network , field (mathematics) , long short term memory , deep learning , linguistics , psychology , cognition , management , sentence , physics , quantum mechanics , philosophy , mathematics , neuroscience , economics , pure mathematics
Semantic Role Labeling (SRL) is a shallow semantic analysis in the field of NLP, and a relatively basic and important step. Traditionally, SRL has been performed based on the results of syntactic analysis and has problems such as over-reliance on feature engineering. With the development of deep learning, many neural network models for NLP have been proposed and SRL tasks can be performed well by neural networks. Among these, long short-term memory networks form a very good fit with the SRL task by virtue of their sequence-based features. In this paper, in order, we first analyze the SRL task based on grammatical analysis and neural networks, then the SRL task based on LSTM and its improved models, then the dataset and model evaluation of the SRL task, and finally conclude and look forward.