The sentiment analysis model with multi-head self-attention and Tree-LSTM

Lei Li; Yijian Pei; Chenyang Jin

doi:10.1117/12.2604779

6 August 2021 The sentiment analysis model with multi-head self-attention and Tree-LSTM

Lei Li, Yijian Pei, Chenyang Jin

Proceedings Volume 11913, Sixth International Workshop on Pattern Recognition; 119130C (2021) https://doi.org/10.1117/12.2604779
Event: Sixth International Workshop on Pattern Recognition, 2021, Chengdu, China

Abstract

In the natural language processing task.We need to extract information from the tree topology. Sentence structure can be achieved by the dependency tree or constituency tree structure to represent.The LSTM can handle sequential information (equivalent to a sequential list), but not tree-structured data.Multi-headed self-attention is used in this model. The main purpose of this model is to reduce the computation and improve the parallel efficiency without damaging the effect of the model.Eliminates the CNN and RNN respectively corresponding to the large amount of calculation, parameter and unable to the disadvantage of parallel computing,keep parallel computing and long distance information.The model combines multi-headed self-attention and tree-LSTM, and uses maxout neurons in the output position.The accuracy of the model on SST was 89%.

Citation Download Citation

Lei Li, Yijian Pei, and Chenyang Jin "The sentiment analysis model with multi-head self-attention and Tree-LSTM", Proc. SPIE 11913, Sixth International Workshop on Pattern Recognition, 119130C (6 August 2021); https://doi.org/10.1117/12.2604779

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