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A Language Modeling Framework For Expert Finding

机译：用于专家查找的语言建模框架

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Statistical language models have been successfully applied to many information retrieval tasks, including expert finding: the process of identifying experts given a particular topic. In this paper, we introduce and detail language modeling approaches that integrate the representation, association and search of experts using various textual data sources into a generative probabilistic framework. This provides a simple, intuitive, and extensible theoretical framework to underpin research into expertise search. To demonstrate the flexibility of the framework, two search strategies to find experts are modeled that incorporate different types of evidence extracted from the data, before being extended to also incorporate co-occurrence information. The models proposed are evaluated in the context of enterprise search systems within an intranet environment, where it is reasonable to assume that the list of experts is known, and that data to be mined is publicly accessible. Our experiments show that excellent performance can be achieved by using these models in such environments, and that this theoretical and empirical work paves the way for future principled extensions.
机译：统计语言模型已成功应用于许多信息检索任务，包括专家查找：给定特定主题的专家识别过程。在本文中，我们介绍并详细介绍了语言建模方法，这些方法将使用各种文本数据源的专家的表示，关联和搜索集成到了生成概率框架中。这提供了一个简单，直观和可扩展的理论框架，以支持对专业知识搜索的研究。为了演示该框架的灵活性，对两种寻找专家的搜索策略进行了建模，这些策略包含从数据中提取的不同类型的证据，然后再扩展为还包含共现信息。所建议的模型是在Intranet环境中的企业搜索系统的上下文中进行评估的，在该环境中可以合理地假设已知专家列表并且要公开获取的数据。我们的实验表明，在这样的环境中使用这些模型可以实现出色的性能，并且这一理论和经验工作为将来的原则性扩展铺平了道路。

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