The experiment shows tree kernel approach is able to give statistical significant improvements over flat syntactic path feature.

Besides, we further propose to leverage on temporal ordering information to constrain the interpretation of discourse relation, which also demonstrate statistical significant improvements for discourse relation recognition on PDTB 2.0 for both explicit and implicit as well.

The experimental results on PDTB v2.0 show that our kernel-based approach is able to give statistical significant improvement over flat syntactic path method.

In addition, we also propose to incorporate temporal ordering information to constrain the interpretation of discourse relations, which also demonstrate statistical significant improvements for discourse relation recognition, both explicit and implicit.

conduct chi square statistical significance test on All relations between flat path approach and Simple-Expansion approach, which shows the performance improvements are statistical significant (p < 0.05) through incorporating tree kernel.

We conduct chi square statistical significant test on All relations, which shows the performance improvement is statistical significant (p < 0.05).

The experiment shows that tree kernel is able to effectively incorporate syntactic structural information and produce statistical significant improvements over flat syntactic path feature for the recognition of both explicit and implicit relation in Penn Discourse Treebank (PDTB; Prasad et al., 2008).

Besides, inspired by the linguistic study on tense and discourse anaphor (Webber, 1988), we further propose to incorporate temporal ordering information to constrain the interpretation of discourse relation, which also demonstrates statistical significant improvements for discourse relation recognition on PDTB v2.0 for both explicit and implicit relations.

Underlining in our method signifies that the differences between correlation coefficients obtained using our method and IMPACT are statistically significant at the 5% significance level.

8 and “All” of Tables 2 and 4, the differences between correlation coefficients obtained using our method and IMPACT are statistically significant at the 5% significance level.

The differences between correlation coefficients obtained using our method and IMPACT are statistically significant at the 5% significance level for adequacy of SMT.

Moreover, the differences between correlation coefficients obtained using our method and other methods are statistically significant at the 5% or lower significance level for adequacy.

Then, we incorporate the model enhanced with topological fields into a natural language generation system that generates constituent orders for German text, and show that the added coherence component improves performance slightly, though not statistically significantly .

We add contextual features using topological field transitions to the model of Filippova and Strube (2007b) and achieve a slight improvement over their model in a constituent ordering task, though not statistically significantly .

Two-tailed sign tests were calculated for each result against the best performing model in each column (1: p = 0.101; 2: p = 0.053; +: statistically significant, p < 0.05; ++: very statistically significant , p < 0.01 ).

We embed entity topological field transitions into their probabilistic model, and show that the added coherence component slightly improves the performance of the baseline NLG system in generating constituent orderings in a German corpus, though not to a statistically significant degree.

In all three cases, we outperform state-of-the-art systems either quantitatively or statistically significantly .

We find that all models outperform comparable state-of-the-art systems either quantitatively or statistically significantly .

We find that Model I performs better than both the best unsupervised system, RACAI (Ion and Tufis, 2007) and the most frequent sense baseline (BmeS), although these differences are not statistically significant due to the small size of the available test data (465).

For both tasks, our models outperform the state-of-the-art systems of the same type either quantitatively or statistically significantly .

system by Li and Sporleder (2009), although not statistically significantly .

Results in bold show statistical significance over baseline in corresponding metric.

When stop words are used the HybHSumg outperforms state-of-the-art by 2.5-7% except R-2 (with statistical significance ).

Results are statistically significant based on t—test.

To determine statistical significance , we obtain p-values from Bikel’s randomized parsing evaluation comparator6, modified for use with tagging accuracy, F-score and dependency accuracy.

The difference in accuracy is only statistically significant between Clark and Curran’s Normal Form model ignoring features and the Petrov parser trained on CCGbank without features (p-value = 0.013).

These results show that the features in CCGbank actually inhibit accuracy (to a statistically significant degree in the case of unlabeled accuracy on section ()0) when used as training data for the Petrov parser.

To check whether changes were statistically significant we applied the test described by Chinchor (1995).

The BFGS, GIS and MIRA models produced mixed results, but no statistically significant decrease in accuracy, and as the amount of parser-annotated data was increased, parsing speed increased by up to 85%.

All changes in F-score are statistically significant .

All of the new models in the table make a statistically significant improvement over the baseline.

We report factors as statistically significant contributors to reading time if the absolute value of the t-value is greater than 2.

The first data column shows the regression on all data; the second and third columns divide the data into open and closed classes, because an evaluation (not reported in detail here) showed statistically significant interactions between word class and 3 of the predictors.

Out of the non-parser-based metrics, word order and bigram probability are statistically significant regardless of the data subset; though reciprocal length and unigram frequency do not reach significance here, likelihood ratio tests (not shown) confirm that they contribute to the model as a whole.

It can be seen that nearly all the slopes have been estimated with signs as expected, with the exception of reciprocal length (which is not statistically significant ).

Statistical significance tests were calculated using the approximate randomization test (Yeh, 2000) with 1000 iterations.

* indicates statistical significance with the column’s Baseline at the p < 0.01 level, T at p < 005.

All numbers are statistically significant * with p-value < 0.01 from the number to their left.

Differences in both precision and recall between the baseline and the other systems are statistically significant at p < 0.01 using the two-tailed Fisher’s exact test.

Differences in both precision and recall between the baseline and the Span-HMM systems are statistically significant at p < 0.01 using the two-tailed Fisher’s exact test.

were not statistically significant , except that the difference in precision between the Multi-Span-HMM and the Span-HMM-Base10 is significant at p < .1.