Improving aspect-level sentiment analysis with aspect extraction
Neural Computing and Applications(2020)
摘要
Aspect-based sentiment analysis (ABSA), a popular research area in NLP, has two distinct parts—aspect extraction (AE) and labelling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesizes that transferring knowledge from a pre-trained AE model can benefit the performance of ALSA models. Based on this hypothesis, word embeddings are obtained during AE and, subsequently, feed that to the ALSA model. Empirically, this work shows that the added information significantly improves the performance of three different baseline ALSA models on two distinct domains. This improvement also translates well across domains between AE and ALSA tasks.
更多查看译文
关键词
ALSA, AE, Knowledge transfer
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络