An exploration of ranking models and feedback method for related entity finding

This article investigates the fundamental problem of traditional language models used for expert finding in bibliometric networks. It introduces novel Venue-Influence Language Modeling methods based on entropy, which can accommodate citation links based weights in an indirect way without using links information. Intuitively, an author publishing in topic-specific venues, either journals or for conferences, will be an expert on a topic as compared to an author publishing in multi-topic venues. The proposed methods are evaluated on real world data, the Digital Bibliography and Library Project (DBLP) dataset to test the performance. Experimental results show that their proposed venue influence language models (ViLMs) based methods outperform the traditional (non-venue based) language models (LM).

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An exploration of ranking models and feedback method for related entity finding

Cited by 3 publications

References 24 publications

Learning contextual representations for entity retrieval

Learning contextual representations for entity retrieval

Relevance-based entity selection for ad hoc retrieval

Venue-Influence Language Models for Expert Finding in Bibliometric Networks

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