An Aspect Term Extraction Method Based on BiLSTM-CRF with BERT Embedding

Abstract

AbstractAspect term extraction is a key task in aspect-level sentiment analysis, aiming at extracting aspect term from text. However, most of the existing aspect-level sentiment analysis methods focus on the matching of aspect term and sentiment polarity, and neglect the research of aspect term extraction. In this paper, an aspect term extraction method based on BiLSTM-CRF with BERT embedding is proposed. Firstly, the embedded layer in Bert model is used to represent the vector, then it is sent to BiLSTM layer for context feature learning, and finally, the aspect term are extracted by CRF. Therefore, the model based on BiLSTM-CRF with BERT embedding is constructed. The experimental results show that this method has high precision and recall rate in Restaurants and Laptop of the SemEval-2014 dataset. The average value of F1 is 85.74%, and the performance of this method has the best comprehensive performance and has more outstanding performance than other models.KeywordsDeep learningSentiment analysisAspect-level sentiment analysisAspect term extraction