Recurrent Neural CRF for Aspect Term Extraction with Dependency Transmission

Abstract

This paper presents a novel neural architecture for aspect term extraction in fine-grained sentiment computing area. In addition to amalgamating sequential features (character embedding, word embedding and POS tagging information), we train an end-to-end Recurrent Neural Networks (RNNs) with meticulously designed dependency transmission between recurrent units, thereby making it possible to learn structural syntactic phenomena. The experimental results show that incorporating these shallow semantic features improves aspect term extraction performance compared to a system that uses no linguistic information, demonstrating the utility of morphological information and syntactic structures for capturing the affinity between aspect words and their contexts.