fulladd and lexfunc significantly outperform stem also in the HR subset (p<.001).

However, the stemploitation hypothesis is dispelled by the observation that both models significantly outperform the stem baseline (p<.001), despite the fact that the latter, again, has good performance, significantly outperforming the corpus-derived vectors (p < .001).

Indeed, if we focus on the third row of Table 5, reporting performance on low stemderived relatedness (LR) items (annotated as described in Section 4.1), fulladd and wadd still significantly outperform the corpus representations (p< .001), whereas the quality of the stem representations of LR items is not significantly different form that of the corpus-derived ones.

0 We have conducted in depth experimental evaluation and showed that the developed methods significantly outperform baseline methods.

As we can see from Figure 5 that all classifiers significantly outperform the majority base-

The learned lessons from this study are: (1) for n-gram usage, the larger the context of the metaphor, the better the classification accuracy becomes; (2) if present source and target information can further boost the performance of the classifiers; (3) LIWC is a useful resource for polarity identification in metaphor-rich texts; (4) analyzing the usages of tense like past vs. present and pronouns are important triggers for positive and negative polarity of metaphors; (5) some categories like family, social presence indicate positive polarity, while others like inhibition, anger and swear words are indicative of negative affect; (6) the built models significantly outperform majority baselines.

Automatic evaluation (using ROUGE (Lin and Hovy, 2003) and BLEU (Papineni et al., 2002)) against manually generated focused summaries shows that our sum-marizers uniformly and statistically significantly outperform two baseline systems as well as a state-of-the-art supervised extraction-based system.

The resulting systems yield results comparable to those from the same system trained on in-domain data, and statistically significantly outperform supervised extractive summarization approaches trained on in-domain data.

In most experiments, it also significantly outperforms the baselines and the extract-based approaches (p < 0.05).

Experiments conducted on a real CQA data show some promising findings: (1) the proposed method significantly outperforms the previous work for question retrieval; (2) the proposed matrix factorization can significantly improve the performance of question retrieval, no matter whether considering the translation languages or not; (3) considering more languages can further improve the performance but it does not seem to produce significantly better performance; (4) different languages contribute unevenly for question retrieval; (5) our proposed method can be easily adapted to the large-scale information retrieval task.

(l) Monolingual translation models significantly outperform the VSM and LM (row 1 and

(3) Our proposed method (leveraging statistical machine translation via matrix factorization, SMT + MF) significantly outperforms the bilingual translation model of Zhou et al.