Formal language models for finding groups of experts |
| |
| Institution: | 1. Pattern Recognition and Human Language Technology (PRHLT) Research Center, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain;2. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Puebla 72840, Mexico;1. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, Spain;2. Departamento de Computación, Universidad Agraria de La Habana, La Habana, Cuba;1. Institute of Computing, Federal University of Amazonas –Av. Gen. Rodrigo Otávio, 3000, Manaus 69077-000, AM, Brazil;2. Neemu S/A, Av. Via Lactea, 1374, Manaus 69060-020, AM, Brazil;1. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Hunan, China;2. Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands |
| |
| 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
