However, when introducing visual features, our performance is comparable (pvalue = 0.058).Furthermore, if we discard visual features but add semantic features from <cite>Bansal et al. (2014)</cite> , we achieve a slight improvement of 0.02 over Bansal2014 (p-value = 0.016), which is largely attributed to the incorporation of word embedding-based features that encode high-level linguistic regularity. Finally, if we enhance our full model with all semantic features from <cite>Bansal et al. (2014)</cite> , our model outperforms theirs by a gap of 0.04 (p-value < 0.01), which justifies our intuition that perceptual semantics underneath visual contents are quite helpful.