SLAOE-NN:A Deep Network with Structure Learning for Aspect and Opining co-Extraction for NLP

Abstract

The task of co-extracting aspects and opinion terms is intended to explicitly extract aspect terms that describe entity features and opinion terms that express emotions from user-generated text. An effective way to accomplish this task is to exploit the relationship between aspect terms and opinion terms by parsing the syntax structure of each sentence. However, this method requires a lot of effort to parse and is highly dependent on the quality of the parsing results. In this paper, we present a deep learning model called SLAOE-NN (Structural Learning and Aspect and Opining Extraction Neural Networks). The proposed model provides an end-to-end solution and does not require any other language resources for preprocessing. Particularly, we use ON-LSTM to generate hidden layer with language structure information which can generate constituency tree unsupervised and we serve it as an auxiliary task for aspect and opinion terms extraction. For aspect terms and opinion terms extract task, we propose different attention mechanism, which can exploit the indirect relationship between aspect term and corresponding opining term to achieve more accurate information extraction. The experimental results of SemEval's three benchmark datasets in 2014 and 2015 show that our model achieves the-state-of-art performance compared to several baselines.