Preference RelationshipBased CrossCMN Scheme for Answer Ranking in Community QA Community question answering (CQA) systems aim to provide users with highquality answers. Nevertheless, unreliable answers are often returned to users in CQA systems, and the phenomenon causes that users have to browse multiple answers to find the best one. To improve such problem, we design a novel scheme, named PWCrossCMN. The scheme ranks the candidate answers by pairwise approach based on numerous historical documents. In the scheme, we apply the preference relationship into deep learning framework. Specifically, the scheme consists of two phases. In phase 1, the scheme extracts the features via automated feature engineering to construct the preference vectors and then divides the vectors into balanced positive and negative training samples based on the preference relationship. In phase 2, we build the CrossCMN model, which implements the multinetwork parallel convolution and the cross forward propagation of fullconnected layers, to achieve training and prediction tasks. Moreover, the multilayer perception (MLP) is introduced to extract combination features in the prediction module. We perform extensive experiments on two typical datasets, and the results show that our scheme has more excellent performance in answer ranking task compared with several stateoftheart baselines. In addition, we have released the relevant codes.

Combining Q&A Pair Quality and Question Relevance Features on Communitybased Question Retrieval The Qu0026A community has become an important way for people to access knowledge and information from the Internet. However, existing translation based models do not consider term weights when assigning weights to query terms in question retrieval. We improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a communitybased question retrieval method that combines question and answer on quality and question relevance (T2LM). We have also proposed a question retrieval method based on convolutional neural networks. The results show that compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91 % and 6.31%.