A system for discovering relationships by feature extraction from text databases
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Expert Finding for Collaborative Virtual Environments
Communications of the ACM
Model-based feedback in the language modeling approach to information retrieval
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Featuring web communities based on word co-occurrence structure of communications: 736
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Query Expansion with Long-Span Collocates
Information Retrieval
Analysis of anchor text for web search
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Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Email as spectroscopy: automated discovery of community structure within organizations
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Use of RDF for expertise matching within academia
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ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Modeling document features for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
A study of the relationship between ad hoc retrieval and expert finding in enterprise environment
Proceedings of the 10th ACM workshop on Web information and data management
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Journal of the American Society for Information Science and Technology
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ECIR'07 Proceedings of the 29th European conference on IR research
Modeling documents as mixtures of persons for expert finding
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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ACM Transactions on Information Systems (TOIS)
Promoting ranking diversity for biomedical information retrieval using wikipedia
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Finding the right supervisor: expert-finding in a university domain
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Information Sciences: an International Journal
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We argue that expert finding is sensitive to multiple document features in an organizational intranet. These document features include multiple levels of associations between experts and a query topic from sentence, paragraph, up to document levels, document authority information such as the PageRank, indegree, and URL length of documents, and internal document structures that indicate the experts’ relationship with the content of documents. Our assumption is that expert finding can largely benefit from the incorporation of these document features. However, existing language modeling approaches for expert finding have not sufficiently taken into account these document features. We propose a novel language modeling approach, which integrates multiple document features, for expert finding. Our experiments on two large scale TREC Enterprise Track datasets, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios. Our main contribution is to develop an effective formal method for modeling multiple document features in expert finding, and conduct a systematic investigation of their effects. It is worth noting that our novel approach achieves better results in terms of MAP than previous language model based approaches and the best automatic runs in both the TREC2006 and TREC2007 expert search tasks, respectively.