Besides, the addition of N always leads to more balanced models, by compensating for the relatively lower precision of B. Due to the lack of a separate test set, as in the original setup by<cite> Klebanov et al. (2014)</cite> , and to the high dimensionality of B's lexicalized features, we cannot rule out over-fitting as an explanation for the relatively good performance of B on this benchmark. In particular, our implementation of the B features performs better than reported by<cite> Klebanov et al. (2014)</cite> on all four genres, namely: 0.52 vs. 0.51 for "news", 0.51 vs. 0.28 for "academic", 0.39 vs. 0.28 for "conversation" and 0.42 vs. 0.33 for "fiction".