Abstract
Bulletin Board Systems (BBS), similar to blogs, newsgroups, online forums, etc., are online broadcasting spaces where people can exchange ideas and make announcements. As BBS are becoming valuable repositories of knowledge and information, effective BBS search engines are required to make the information universally accessible and useful. However, the techniques that have been proven successful for web search are not suitable for searching BBS articles due to the nature of BBS. In this paper, we propose a novel article language model (LM) to build an effective BBS search engine. We investigate the differences between BBS articles and web pages, then extend the traditional LM to author LM and category LM. The article LM is powerful in the sense that it can combine the three LMs into a single framework. Experimental results shows that our article LM substantially outperforms both INQUERY algorithm and the traditional LM.
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