Research of semantic role labeling based on long short-term memory neural networks
Xuheng Liang · Journal of Physics Conference Series · 2021
Abstract 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.