UH-PRHLT at SemEval-2016 Task 3:Combining Lexical and Semantic-based Features forCommunity Question Answering
Table 1: Results of Subtask A: English Question-Comment Similarity. (a) Baselines; (b) proposed approach.
['[EMPTY]', '[BOLD] Model', '[BOLD] Ranking measures  [BOLD] MAP', '[BOLD] Ranking measures  [BOLD] AvgRec', '[BOLD] Ranking measures  [BOLD] MRR', '[BOLD] Classification measures  [BOLD] Acc.', '[BOLD] Classification measures  [BOLD] P', '[BOLD] Classification measures  [BOLD] R', '[BOLD] Classification measures  [BOLD] F1']
The best results per partition and subtask are highlighted in bold. In addition, we always refer to the run with the highest performance. Finally, our percentage comparisons use always absolute values. In terms of ranking measures, our system outperformed both the random and the search engine baseline. Using the development set, we observed a MAP improvement of 9.4% compared with the results obtained by the search engine. We can see similar differences with respect to the other two ranking measures. Classification results are also superior. We obtain improvements in accuracy and F1 of 24.9% and 5.2% respectively. These results manifest the potential of the selected lexical and semantic-based features for this subtask.