Question retrieval aims to increase the accessibility of the community Question Answer (cQA) archives and has attracted increasing research interests recently. In this paper, we present a novel method for improving the question retrieval performance by investigating the question term selection and weighting as well as reranking results. Different from previous work, we propose a hierarchical question classification method with a sparse regularization to mimc user's question labeling in cQAs. Based on the hierarchical classification, we explore the local context of the question for term selection and reranking results and then integrating them into our proposed general question retrieval framework. The experimental results on a Yahoo! Answers dataset show the effectiveness of our method as compared to existing general question retrieval models and some state-of-the-art methods of utilizing category information for question retrieval.

Aspect identification is a fundamental basic process for web opinion analysis of product reviews, which shows great potential in many real applications of e-commerce. The main stream of the solutions of aspects identification is to explore the modification relationship between aspect and opinion words. In this paper, we investigate the complexity of the review sentence to better capture the modification relations. Then, we propose a language model based aspect identification method integrated with a translation model to exploit these modification relations for performance improvement. The experimental results on 11 popular products in four domains from various web sites show our approach is more effective compared with some strong baselines and the state-of-the-art methods.

We present a hierarchical kernelized classification model for the automatic classification of general questions into their corresponding topic categories in community Question Answering service (cQAs). This could save many efforts of manual classification and facilitate browsing as well as better retrieving of questions from the cQA archives. To deal with the challenge of short text message of questions, we explore and optimally combine various cQA features by introducing multiple kernel learning strategy into the hierarchical classification framework. We propose a hybrid regularization approach of combining orthogonal constraint and L1 sparseness in our framework to promote the discriminative power on similar topics as well as sparsing the model parameters. The experimental results on a real world dataset from Yahoo! Answers demonstrate the effectiveness of our proposed model as compared to the state-of-the-art methods and strong baselines.