Formal language models for finding groups of experts. Issue 4 (July 2016)

• Record Type: Journal Article
• Authors: Liang, Shangsong; de Rijke, Maarten
• Is Part Of: Information processing & management. Volume 52:Issue 4(2016:Jul.)
• Page Start: 529
• Page End: 549
• Publication Date: 2016-07
• Subjects: Group finding -- Entity retrieval -- Enterprise search; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2015.11.005
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We introduce a new information retrieval task: given a topic, try to find knowledgeable groups that have expertise on the topic. Five probabilistic language models are proposed to tackle the challenge of automatically finding groups of experts in heterogeneous document collections. For evaluation purpose, a data set is created based on a publicly downloadable corpus used in the TREC Enterprise 2005 and 2006 tracks and three types of ground truth are defined. We provide a detailed analysis of the performance of the proposed group finding models. Abstract: The task of finding groups or teams has recently received increased attention, as a natural and challenging extension of search tasks aimed at retrieving individual entities. We introduce a new group finding task: given a query topic, we try to find knowledgeable groups that have expertise on that topic. We present five general strategies for this group finding task, given a heterogenous document repository. The models are formalized using generative language models. Two of the models aggregate expertise scores of the experts in the same group for the task, one locates documents associated with experts in the group and then determines how closely the documents are associated with the topic, whilst the remaining two models directly estimate the degree to which a group is a knowledgeable group for a given topic. For evaluation purposes we construct a test collection based on the TREC 2005 and 2006 Enterprise collections, and define three types of ground truth for our task. Experimental results show that our five knowledgeable group finding models achieve high absolute scores. We also find significant differences between different ways of estimating the association between a topic and a group.