Despite statistically significant predictors of deception such as shorter talking time, fewer semantic details, and less coherent statements, DePaulo et al.

and revealed that verbal cues based on lexical categories extracted using the LIWC tool show statistically significant , though small, differences between truth- and lie-tellers.

Statistically significant results are in bold.

performs best, with 59.5% cross-validation accuracy, which is a statistically significant improvement over the baselines of LR (75(4) 2 22.25, p < .0001), and NB (25(4) 2 16.19,]?

In comparison with classification accuracy on pooled data, a paired t-test shows statistically significant improvement across all age groups using RF, 75(3) = 1037,]?

There is no statistically significant difference between DocSys and DocBase generations for METEOR and BLEU—4.4 However, there is a statistically significant difference in the syntactic variability metric for both domains (weather - X2=l37.16, d.f.=1, p<.0001; biography - X2=96.641, d.f.=1, p<.

In terms of significance, there are no statistically significant differences between the systems for weather (DocOrig vs. DocSyS - X2=.347, d.f.=l, p=.555; DocOrig vs. DocBase - X2=.090, d.f.=l, p=.764; DocSyS vs. DocBase - X2=.790, d.f.=l, p=.373).

For biography, the trend fits nicely both numerically and in terms of statistical significance (DocOrig vs. DocSys -X2=5 .094, d.f.=l, p=.

Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e. g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.

We evaluate the summarization models on the standard Document Understanding Conference (DUC) 2006 and 2007 corpora 2 for query-focused MDS and find that all of our compression-based summarization models achieve statistically significantly better performance than the best DUC 2006 systems.

With these results we believe we are the first to successfully show that sentence compression can provide statistically significant improvements over pure extraction-based approaches for query-focused MDS.

Our sentence-compression-based systems (marked with T) show statistically significant improvements over pure extractive summarization for both R-2 and R-SU4 (paired t-test, p < 0.01).

In Table 7, our context-aware and head-driven tree-based compression systems show statistically significantly (p < 0.01) higher precisions (Uni-

For grammatical relation evaluation, our head-driven tree-based system obtains statistically significantly (p < 0.01) better Fl score (Rel-F1 than all the other systems except the rule-based system).

To determine statistical significance of the improvements, we also compute paired, one-tailed t tests.

‘i’(‘*’) in the top four lines indicates statistical significance at p < 0.001 (0.05) when compared with the previous row.

‘i’ or ‘*’ in the top four rows indicates statistical significance at p < 0.001 or < 005 compared with the previous row.

The model average is statistically significantly different from all the other conditions p < 10—7 (Study 1).

The averages of the tested conditions are shown in Table 2, and are statistically significant .

The differences in density between the human average and the non-baseline conditions are highly statistically significant , according to paired two-tailed Wilcoxon signed-rank tests for the statistic calculated for each topic cluster.

Indeed, the difference between our best results and those of Elsner and Charniak are not statistically significant .

However, this improvement is not statistically significant .

The best results, that present a statistically significant improvement when compared to the random baseline, are obtained when distance information and the number of entities “shared” by two sentences are taken into account (PW).

All correlation coefficients are statistically significant (p < 0.01, N = 435).

Differences in correlation coefficients between models with two versus one modality are all statistically significant (p < 0.01 using a t-test), with the exception of Concat when compared against VisAttr.

All correlation coefficients are statistically significant (p < 0.01, N = 1,716).

All correlation coefficients are statistically significant (p < 0.01, N = 435).

(2) Taking advantage of potentially rich semantic information drawn from other languages via statistical machine translation, question retrieval performance can be significantly improved (row 3, row 4, row 5 and row 6 vs. row 7, row 8 and row 9, all these comparisons are statistically significant at p < 0.05).

(2012) (row 7 vs. row 8, the comparison is statistically significant at p < 0.05).

(1) Our proposed matrix factorization can significantly improve the performance of question retrieval (row 1 vs. row2; row3 vs. row4, the improvements are statistically significant at p < 0.05).

When compared to a state-of-the-art open-domain headline abstraction system (Filippova, 2010), the new headlines are statistically significantly better both in terms of readability and informativeness.

0 Amongst the automatic systems, HEADY performed better than MSC, with statistical significance at 95% for all the metrics.

o The most frequent pattern baseline and HEADY have comparable performance across all the metrics (not statistically significantly different), although HEADY has slightly better scores for all metrics except for informativeness.

The starred results are statistically significant improvements over the Baseline (at confidence p < 0.05).

This improvement is statistically significant at confidence p < 0.05, which we computed using the pairwise bootstrap resampling technique of Koehn (2004).

However this improvement (0.34) is not statistically significant .

To evaluate our method, we use the word clusters in an NER system and demonstrate a statistically significant improvement in F1 score when using bilingual word clusters instead of monolingual clusters.

For English and Turkish we observe a statistically significant improvement over the monolingual model (cf.

English again has a statistically significant improvement over the baseline.

to compute MAP values and corresponding statistical significance , we randomly split each test set into 30 subsets.

This improvement is statistically significant at p < 0.01 for BInc and Lin, and p < 0.015 for Cosine, using paired t-test.

On test-setivc, where context mismatches are abundant, our model outperformed all other baselines ( statistically significant at p < 0.01).

The evaluations indicate statistical significance , but the test settings are relatively specific.

The statistical significance is particularly high, even though there were several limitations in the experimental setting.

According to the one-sided Wilcoxon rank-sum test, both Collective Funniness and all Upper Agreements increase from FORM to FORM+TABOO and from FORM+TABOO to FORM+TABOO+CONT statistically significantly (in all cases p < .002).

The improvement in precision when using TR&EX is statistically significant (p < 0.05).9 Note that F-measure dropped

The improvement in precision when using TR&EX is statistically significant (p < 0.01).

statistically significant in both settings (p < 0.01).