Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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
Evaluating high accuracy retrieval techniques
Proceedings of the 27th 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
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Broad expertise retrieval in sparse data environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise drift and query expansion in expert search
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
The CSIRO enterprise search test collection
ACM SIGIR Forum
A few examples go a long way: constructing query models from elaborate query formulations
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Voting techniques for expert search
Knowledge and Information Systems
A language modeling framework for expert finding
Information Processing and Management: an International Journal
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
High quality expertise evidence for expert search
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Associating people and documents
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Modeling documents as mixtures of persons for expert finding
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Using transactional data from ERP systems for expert finding
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Finding relevant information of certain types from enterprise data
Proceedings of the 20th ACM international conference on Information and knowledge management
Foundations and Trends in Information Retrieval
Information Technology and Management
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The task addressed in this paper, finding experts in an enterprise setting, has gained in importance and interest over the past few years. Commonly, this task is approached as an association finding exercise between people and topics. Existing techniques use either documents (as a whole) or proximity-based techniques to represent candidate experts. Proximity-based techniques have shown clear precision-enhancing benefits. We complement both document and proximity-based approaches to expert finding by importing global evidence of expertise, i.e., evidence obtained using information that is not available in the immediate proximity of a candidate expert's name occurrence or even on the same page on which the name occurs. Examples include candidate priors, query models, as well as other documents a candidate expert is associated with. Using the CERC data set created for the TREC 2007 Enterprise track we identify examples of non-local evidence of expertise. We then propose modified expert retrieval models that are capable of incorporating both local (either document or snippet-based) evidence and non-local evidence of expertise. Results show that our refined models significantly outperform existing state-of-the-art approaches.