Aspect term extraction via information-augmented neural network

Abstract

Aspect term extraction (ATE) aims at identifying the aspect terms that are expressed in a sentence. Recently, Seq2Seq learning has been employed in ATE and significantly improved performance. However, it suffers from some weaknesses, such as lacking the ability to encode the more informative information and integrate information of surrounding words in the encoder. The static word embeddings employed in ATE fall short of modeling the dynamic meaning of words. To alleviate the problems mentioned above, this paper proposes the information-augmented neural network (IANN) which is a novel Seq2Seq learning framework. In IANN, a specialized neural network is developed as the key module of the encoder, named multiple convolution with recurrence network (MCRN), to encode the more informative information and integrate information of surrounding words in the encoder. The contextualized embedding layer is designed to capture the dynamic word sense. Besides, the novel AO ({Aspect, Outside}) tags are proposed as the less challenging tagging scheme. A lot of experiments have been performed on three widely used datasets. These experiments demonstrate that the proposed IANN acquires state-of-the-art results and validate that the proposed IANN is a powerful method for the ATE task.