Structural Normalisation Methods for Improving Best Answer Identification in Question Answering Communities

Nowadays, Question Answering (Q&A) websites are popular source of information for finding answers to all kind of questions. Due to this popularity it is critical to help the identification of best answers to existing questions for simplifying the access to relevant information. Although it is possible to identify relatively accurately best answers by using binary classifiers coupled with user, content and thread} features, existing works have generally ignored to incorporate the thread-like structure of Q&A communities in the design of best answer identification predictors and algorithms. This paper investigates this particular issue by studying structural normalisation techniques for improving the accuracy of feature based best answer identification models. Thread-based normalisation methods are introduced for improving the accuracy of identification models by introducing a systematic normalisation approach that normalise predictors by taking into account relations between features and the thread-like structure of Q&A communities. Compared to similar non normalised models, better results are obtained for each of the three communities studied. These results show that structural normalisation methods can improve the identification of best answers compared to non-normalised models.