Speculation and negation are important in-formation to identify text factuality. In this paper, we propose a Convolutional Neural Network (CNN)-based model with probabil-istic weighted average pooling to address speculation and negation scope detection. In particular, our CNN-based model extracts those meaningful features from various syn-tactic paths between the cues and the candi-date tokens in both constituency and depen-dency parse trees. Evaluation on BioScope shows that our CNN-based model significant-ly outperforms the state-of-the-art systems on Abstracts, a sub-corpus in BioScope, and achieves comparable performances on Clini-cal Records, another sub-corpus in BioScope.