A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Non-local evidence for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
A language modeling framework for expert finding
Information Processing and Management: an International Journal
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning Aggregation Functions for Expert Search
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
The influence of the document ranking in expert search
Information Processing and Management: an International Journal
Query modeling for entity search based on terms, categories, and examples
ACM Transactions on Information Systems (TOIS)
Foundations and Trends in Information Retrieval
Expertise retrieval in bibliographic network: a topic dominance learning approach
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Since the introduction of the Enterprise Track at TREC in 2005, the task of finding experts has generated a lot of interest within the research community. Numerous models have been proposed that rank candidates by their level of expertise with respect to some topic. Common to all approaches is a component that estimates the strength of the association between a document and a person. Forming such associations, then, is a key ingredient in expertise search models. In this paper we introduce and compare a number of methods for building document-people associations. Moreover, we make underlying assumptions explicit, and examine two in detail: (i) independence of candidates, and (ii) frequency is an indication of strength. We show that our refined ways of estimating the strength of associations between people and documents leads to significant improvements over the state-of-the-art in the end-to-end expert finding task.