Finding knowledgeable groups in enterprise corpora

Shangsong Liang, M. de Rijke
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引用次数: 14

Abstract

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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在企业语料库中寻找知识渊博的群体
查找组的任务是旨在检索单个实体的搜索任务的自然扩展。我们引入了一个组查找任务:给定一个查询主题,查找具有该主题专业知识的知识组。为此,我们提出了四项总体策略。这些模型使用生成语言模型形式化。其中两个模型汇总同一组中专家的专业知识分数,一个定位与组中专家相关的文档,然后确定文档与主题的关联程度,而其余模型直接估计一个组在多大程度上是给定主题的知识组。我们基于TREC 2005和2006 Enterprise集合构建了一个测试集合。我们发现估算主题和群体之间关联的不同方法之间存在显著差异。实验表明,我们的知识群发现模型获得了很高的绝对分数。
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