Negation scope detection with recurrent neural networks models in review texts

Abstract

Identifying negation scopes in a text is an important subtask of information extraction that can benefit other natural language processing tasks, like relation extraction, question answering and sentiment analysis, and serves the task of social media text understanding. The task of negation scope detection can be regarded as a token-level sequence labelling problem. In this paper, we propose different models based on recurrent neural networks (RNNs) and word embedding that can be successfully applied to such tasks without any task-specific feature engineering effort. Our experimental results show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based model.