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  • Publication . Conference object . 2013
    Open Access English
    Authors: 
    Shangsong Liang; Maarten de Rijke;
    Country: Netherlands
    Project: NWO | Modeling and Learning fro... (2300171779), NWO | SPuDisc: Searching Public... (2300176811), NWO | Building Rich Links to En... (2300153702), EC | LIMOSINE (288024), EC | PROMISE (258191)

    The task of finding groups is a natural extension of search tasks aimed at retrieving individual entities. We introduce a group finding task: given a query topic, find knowledgeable groups that have expertise on that topic. We present four general strategies to this task. 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 model directly estimates the degree to which a group is a knowledgeable group for a given topic. We construct a test collections based on the TREC 2005 and 2006 Enterprise collections. We find significant differences between different ways of estimating the association between a topic and a group. Experiments show that our knowledgeable group finding models achieve high absolute scores.

Include:
1 Research products, page 1 of 1
  • Publication . Conference object . 2013
    Open Access English
    Authors: 
    Shangsong Liang; Maarten de Rijke;
    Country: Netherlands
    Project: NWO | Modeling and Learning fro... (2300171779), NWO | SPuDisc: Searching Public... (2300176811), NWO | Building Rich Links to En... (2300153702), EC | LIMOSINE (288024), EC | PROMISE (258191)

    The task of finding groups is a natural extension of search tasks aimed at retrieving individual entities. We introduce a group finding task: given a query topic, find knowledgeable groups that have expertise on that topic. We present four general strategies to this task. 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 model directly estimates the degree to which a group is a knowledgeable group for a given topic. We construct a test collections based on the TREC 2005 and 2006 Enterprise collections. We find significant differences between different ways of estimating the association between a topic and a group. Experiments show that our knowledgeable group finding models achieve high absolute scores.

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