A Probabilistic Model for Fine-Grained Expert Search

Proceedings of ACL-08: HLT, pages 914–922, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics A Probabilistic Model for Fine-Grained Expert Search Shenghua Bao1, Huizhong Duan1, Qi Zhou1, Miao Xiong1, Yunbo Cao1,2, Yong Yu1 1Shanghai Jiao Tong University, 2Microsoft Research Asia Shanghai, China, 200240 Beijing, China, 100080 {shhbao,summer,jackson,xiongmiao,yyu} @apex.sjtu.edu.cn yunbo.cao@microsoft.com Abstract Expert search, in which given a query a ranked list of experts instead of documents is returned, has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been pro- posed, including metadata extraction, expert profile building, and formal model generation. However, all of them conduct expert search with a coarse-grained approach. With these, further improvements on expert search are hard to achieve. In this paper, we propose conducting expert search with a fine-grained approach. Specifically, we utilize more spe- cific evidences existing in the documents. An evidence-oriented probabilistic model for ex- pert search and a method for the implementa- tion are proposed. Experimental results show that the proposed model and the implementa- tion are highly effective. 1 Introduction Nowadays, team work plays a more important role than ever in problem solving. For instance, within an enterprise, people handle new problems usually by leveraging the knowledge of experienced col- leagues. Similarly, within research communities, novices step into a new research area often by learning from well-established researchers in the research area. All these scenarios involve asking the questions like “who is an expert on X?” or “who knows about X?” Such questions, which cannot be answered easily through traditional document search, raise a new requirement of searching people with certain expertise. To meet that requirement, a new task, called ex- pert search, has been proposed and studied inten- sively. For example, TREC 2005, 2006, and 2007 provide the task of expert search within the enter- prise track. In the TREC setting, expert search is defined as: given a query, a ranked list of experts is returned. In this paper, we engage our study in the same setting. Many approaches to expert search have been proposed by the participants of TREC and other researchers. These approaches include metadata extraction (Cao et al., 2005), expert profile build- ing (Craswell, 2001, Fu et al., 2007), data fusion (Maconald and Ounis, 2006), query expansion (Macdonald and Ounis, 2007), hierarchical lan- guage model (Petkova and Croft, 2006), and for- mal model generation (Balog et al., 2006; Fang et al., 2006). However, all of them conduct expert search with what we call a coarse-grained ap- proach. The discovering and use of evidence for expert locating is carried out under a grain of document. With it, further improvements on expert search are hard to achieve. This is because differ- ent blocks (or segments) of electronic documents usually present different functions and qualities and thus different impacts for expert locating. In contrast, this paper is concerned with propos- ing a probabilistic model for fine-grained expert search. In fine-grained expert search, we are to extract …

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