@inproceedings{wu2014improving,
author = {Wu, Haocheng and Wu, Wei and Zhou, Ming and Chen, Enhong and Duan, Lei and Shum, Heung-Yeung and Wu, Wei},
title = {Improving search relevance for short queries in community question answering},
booktitle = {Proceedings of the 7th ACM international conference on Web search and data mining (WSDM'14)},
year = {2014},
month = {February},
abstract = {Relevant question retrieval and ranking is a typical task in community question answering (CQA). Existing methods mainly focus on long and syntactically structured queries. However, when an input query is short, the task becomes challenging, due to a lack information regarding user intent. In this paper, we mine different types of user intent from various sources for short queries. With these intent signals, we propose a new intent-based language model. The model takes advantage of both state-of-the-art relevance models and the extra intent information mined from multiple sources. We further employ a state-of-the-art learning-to-rank approach to estimate parameters in the model from training data. Experiments show that by leveraging user intent prediction, our model significantly outperforms the state-of-the-art relevance models in question search.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/improving-search-relevance-short-queries-community-question-answering/},
pages = {43-52},
edition = {Proceedings of the 7th ACM international conference on Web search and data mining (WSDM'14)},
}