Learning to recognize opinion targets using recurrent neural networks

Opinion target recognition can be deemed a problem of token-level sequence labelling, and each word in the sentence is assigned to a label with the standard BIO tagging scheme. Among a variety of methods, recently emerged recurrent neural network (RNN) is considered more effective to deal with such kinds of sequence annotation problem. Whereas existing RNN models mainly focus on learning the dependency relationships in the input sequence while ignoring the ones in the output sequence. To this end, we proposed an augmented RNN model called OLSRNN. The OLSRNN model adds self-connections to the output layer on the basics of conventional RNN models to further capture output temporal dependencies. Over the benchmark customer review datasets, experiment results demonstrate the effectiveness of the proposed approach in opinion target recognition in comparison with other baseline methods.