@techreport{TD:100545,
	att_abstract={{Online forums contain interactive and semantically related discussions on various questions. Extracted question-answer archive is invaluable knowledge, which can be used to im- prove Question Answering services. In this paper, we address the problem of Question Suggestion, which targets at sug- gesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm be- tween semantically related questions. Therefore, we present a new framework to suggest questions, and propose the Topic- enhanced Translation-based Language Model (TopicTRLM) which fuses both the lexical and latent semantic knowledge. Extensive experiments have been conducted with a large real world data set. Experimental results indicate our approach is very effective and outperforms other popular methods in sev- eral metrics.}},
	att_authors={ik089v},
	att_categories={C_IIS.2, C_CCF.5, C_IIS.3, C_IIS.4},
	att_copyright={{AAAI}},
	att_copyright_notice={{The definitive version was published in AAAI-2011. {{, 2011-08-07}}{{, http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3672}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={question suggestions,  online forum,  machine learning},
	att_techdoc={true},
	att_techdoc_key={TD:100545},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100545_DS1_2011-06-02T00:01:27.658Z.pdf},
	author={Tom Chao Zhou and Chin-Yew Lin and Irwin King and Michael R. Lyu and Young-In Song and Yunbo Cao},
	institution={{AAAI-2011}},
	month={August},
	title={{Learning to Suggest Questions in Online Forums}},
	year=2011,
}