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We describe our submission to SemEval-2016 Task 3 on Community Question Answering. We participated in subtask A, which asks to rerank the comments from the thread for a given forum question from good to bad. Our approach focuses on the generation and use of goodness polarity lexicons, similarly to the sentiment polarity lexicons, which are very popular in sentiment analysis. In particular, we use a combination of bootstrapping and pointwise mutual information to estimate the strength of association between a word (from a large unannotated set of question-answer threads) and the class of good/bad comments. We then use various features based on these lexicons to train a regression model, whose predictions we use to induce the final comment ranking. While our system was not very strong as it lacked important features, our lexicons contributed to the strong performance of another top-performing system.

PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering