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 be-tween 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.