Experimental results on benchmark data show that our method significantly outperforms the baseline supervised parser and other entire-tree based semi-supervised methods, such as self-training, co-training and tri-training.

Using unlabeled data with the results of ZPar (“Unlabeled <— Z”) significantly outperforms the baseline GParser by 0.30% (93.15-82.85) on English.

However, we find that although the parser significantly outperforms the supervised GParser on English, it does not gain significant improvement over co-training with ZPar (“Unlabeled <— Z”) on both English and Chinese.

(2012) and Bohnet and Nivre (2012) use joint models for POS tagging and dependency parsing, significantly outperforming their pipeline counterparts.

Our empirical results have shown the probabilistic labeling approach significantly outperforms a previous graph-matching approach for referential grounding.

significantly outperforms state-space search (S.S.S.

Although probabilistic labeling significantly outperforms the state-space search, the grounding performance is still rather poor (less than 50%)

Our empirical results have shown that the probabilistic labeling approach significantly outperforms the state-space search approach in both grounding accuracy and efficiency.

In experiments, we show that our model significantly outperforms strong linear and nonlinear discriminative baselines on three datasets under various settings.

Focusing on the three financial crisis related datasets, the proposed model significantly outperform the standard linear regression method in statistics and strong discriminative support vector regression baselines.

By varying different experimental settings on three datasets concerning different periods of the Great Recession from 2006-2013, we empirically show that our approach significantly outperforms the baselines by a wide margin.

0 Our results significantly outperform standard linear regression and strong SVM baselines.

In both tasks, our method significantly outperforms competitive baselines and returns results that are statistically comparable to current state-of-the-art methods, while requiring no task-specific customisations.

Except for the DE setting in which Proposed method significantly outperforms both SFA and SCL, the performance of the Proposed method is not statistically significantly different to that of SFA or SCL.

Without requiring any task specific customisations, systems based on our distribution prediction method significantly outperform competitive baselines in both tasks.

Experiments on Automatic Content Extraction (ACE)1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.

Experiments demonstrated our approach significantly outperformed pipelined approaches for both tasks and dramatically advanced state-of-the-art.

We can see that our approach significantly outperforms the pipelined approach for both tasks.

Automatic and manual evaluation results over meeting, chat and email conversations show that our approach significantly outperforms baselines and previous extractive models.

Results indicate that our system significantly outperforms baselines in overall quality and responsiveness, for both meeting and email datasets.

Automatic evaluation on the chat dataset and manual evaluation over the meetings and emails show that our system uniformly and statistically significantly outperforms baseline systems, as well as a state-of-the-art query-based extractive summarization system.

We can see that the Unbalanced approach significantly outperforms the baseline (Balanced).

We can also notice that the prediction model applied to the balanced corpus (Balanced + Prediction) slightly outperforms the baseline while the Unbalanced + Prediction approach significantly outperforms the three other approaches (moreover the variation observed with the Unbalanced approach are lower than the Unbalanced —|— Prediction approach).

As for the previous experiment, we can see that the Unbalanced approach significantly outperforms the Balanced approach.

that PR significantly outperforms all other baselines in both the CR dataset and the MD dataset (average accuracy across domains is reported).

In contrast, both PR1“ and PR significantly outperform CRF, which implies that incorporating lexical and discourse constraints as posterior constraints is much more effective.

We can see that both PR and Ple significantly outperform all other baselines in all domains.