Target Extraction via Feature-Enriched Neural Networks Model

Target extraction is an important task in target-based sentiment analysis, which aims at identifying the boundary of target in given text. Previous works mainly utilize conditional random field (CRF) with a lot of handcraft features to recognize the target. However, it is hard to manually extract effective features to boost the performance of CRF-based methods. In this paper, we employ gated recurrent units (GRU) with label inference, to find valid label path for word sequence. At the same time, we find that character-level features play important roles in target extraction, and represent each word by concatenating word embedding and character-level representations which are learned via character-level GRU. Further, we capture boundary features of each word from its context words by convolution neural networks to assist the identification of the target boundary, since the boundary of a target is highly related to its context words. Experiments on two datasets show that our model outperforms CRF-based approaches and demonstrate the effectiveness of features learned from character-level and context words.