Community-based question-answering (CQA) services contribute to solving many difficult questions we have. For each question in such services, one best answer can be designated, among all answers, often by the asker. However, many questions on typical CQA sites are left without a best answer even if when good candidates are available. In this paper, we attempt to address the problem of predicting if an answer may be selected as the best answer, based on learning from labeled data. The key tasks include designing features measuring important aspects of an answer and identifying the most importance features. Experiments with a Stack Overflow dataset show that the contextual information among the answers should be the most important factor to consider.

For understanding humorous dialogue, a collection of humorous expressions is needed. In addition to humorous expressions, their annotations are important to be used as language resources. In this paper, we analyzed how human assessors annotate humorous expressions extracted from an online community-based question-answering (CQA) corpus, which contains many interesting examples of humorous communication. We analyzed the annotation results of a collection of humorous expressions as done by 28 annotators in terms of the degree of humor and categorization of humor. We found the assessments to be quite subjective, and only marginal inter-annotator agreements were observed. This result suggests that the variability in humor annotations is not noise resulting from erroneous assessment but is rooted in personality differences of the annotators. It would be necessary to incorporate the individual differences in humor perception for properly utilizing the resources. We discuss the possibility to improve the collection process by applying filtering techniques.