Speculation and negation scope detection via convolutional neural networks

Abstract

Speculation and negation are important information to identify text factuality. In this paper, we propose a Convolutional Neural Network (CNN)-based model with probabilistic weighted average pooling to address speculation and negation scope detection. In particular, our CNN-based model extracts those meaningful features from various syntactic paths between the cues and the candidate tokens in both constituency and dependency parse trees. Evaluation on BioScope shows that our CNN-based model significantly outperforms the state-of-the-art systems on Abstracts, a sub-corpus in BioScope, and achieves comparable performances on Clinical Records, another sub-corpus in BioScope.