The experimental results demonstrate that our model is able to significantly outperform the state-of-the-art coherence model by Barzilay and Lapata (2005), reducing the error rate of the previous approach by an average of 29% over three data sets against human upper bounds.

From the curves, our model consistently performs better than the baseline with a significant gap, and the combined model also consistently and significantly outperforms the other two.

When applied to distinguish a source text from a sentence-reordered permutation, our model significantly outperforms the previous state-of-the-art,

Double (**) and single (*) asterisks indicate that the respective model significantly outperforms the baseline at p < 0.01 and p < 0.05, respectively.

Comparing these accuracies to the baseline, our model significantly outperforms the baseline with p < 0.01 in the WSJ and Earthquakes data sets with accuracy increments of 2.35% and 2.91%, respectively.

The combined model in all three data sets gives the highest performance in comparison to all single models, and it significantly outperforms the baseline model with p < 0.01.

Adaptation experiments on LETOR3.0 data set demonstrate that query weighting significantly outperforms document instance weighting methods.

We evaluated our approaches on LETOR3.0 dataset for ranking adaptation and found that: (l) the first method efficiently estimate query weights, and can outperform the document instance weighting but some information is lost during the aggregation; (2) the second method consistently and significantly outperforms document instance weighting.

wise approach significantly outperformed pointwise approach, which takes each document instance as independent learning object, as well as pairwise approach, which concentrates learning on the order of a pair of documents (Liu, 2009).