This paper shows that semantic classes help to obtain significant improvement in both parsing and PP attachment tasks.

The combination of word sense and first-level hypernyms produced a significant improvement over their basic model.

This paper shows that semantic classes achieve significant improvement both on full parsing and PP attachment tasks relative to the baseline parsers.

The results are highly significant in demonstrating that a simplistic approach to incorporating lexical semantics into a parser significantly improves parser performance.

This paper shows that semantic classes help to obtain significant improvements for both PP attachment and parsing.

The results are notable in demonstrating that very simple preprocessing of the parser input facilitates significant improvements in parser performance.

The results are similar to IST, with significant improvements for verbs.

In this paper, we reexamine this problem using recently proposed axiomatic approaches and find that, with appropriate term weighting strategy, we are able to exploit the information from lexical resources to significantly improve the retrieval performance.

In this paper, we reexamine the problem of query expansion using lexical resources in recently proposed axiomatic framework and find that we are able to significantly improve retrieval performance through query expansion using only handcrafted lexical resources.

By incorporating this similarity function into the axiomatic retrieval models, we show that query expansion using the information from only WordNet can lead to significant improvement of retrieval performance, which has not been shown in the previous studies (Voorhees, 1994; Stairmand, 1997).

QEdef significantly improves the retrieval performance for all the data sets.

Using this similarity function in query expansion can significantly improve the retrieval performance.

Unlike previous studies, we are able to show that query expansion using only manually created lexical resources can significantly improve the retrieval performance.

That is, we test for significant improvements in GMAP by applying the Wilcoxon signed rank test to the paired, transformed average precisions, log AP.

This represents significant improvements over IBM transcripts used in earlier CL-SR evaluations, which had a best reported WER of 39.6% (Byrne et al., 2004).

combination is a statistically significant improvement (04 = 0.05) over our new transcript set (that is, over the best single transcript result).

Tests for statistically significant improvements in GMAP are computed using our paired log AP test, as discussed in Section 4.2.2.

One surprising observation from Table 1 is that the mean improvement in log AP for interleaving is fairly large and yet not statistically significant (it is in fact a larger mean improvement than several other baseline combination approaches which are significant improvements .

The results show that only the RL strategy leads to significantly improved user ratings (increasing average Task Ease by 49% and Future Use by 19%), whereas the ratings for the SL policy are not significantly better than those for the WOZ data, see Table 3.

Table 1: Predicted accuracy for presentation timing and modality (with standard deviation i), * denotes statistically significant improvement at p < .05

For presentation timing, none of the classifiers produces significantly improved results.

data show significantly improved Task Ease, better presentation timing, more agreeable verbal and multimodal presentation, and that more users would use the RL-based system in the future (Future Use).

Moreover, subjective words can significantly improve accuracy.

Our empirical evaluations show that subjective words and phrases can significantly improve email summarization.

Their experiments showed that features about emails and the email thread could significantly improve the accuracy of summarization.

Turney (2006b) achieves 56% accuracy on this dataset, which matches the average human performance of 57%, and represents a significant improvement over the 20% random-guessing baseline.

Both results represent a statistically significant improvement over the majority class baseline and over using sentence words only, and a slight improvement over the best type A and type 0 systems on SemEval ’07, which achieved 66% and 67% accuracy, respectively.4

As we can see, using prepositions alone yields about 33% accuracy, which is a statistically significant improvement over the maj ority-class baseline.