TREC and TIPSTER experiments with INQUERY
TREC-2 Proceedings of the second conference on Text retrieval conference
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
International Journal of Human-Computer Studies
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
CLEF 2008: ad hoc track overview
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
CLEF 2009 ad hoc track overview: TEL and Persian tasks
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
CLEF 2009 ad hoc track overview: TEL and Persian tasks
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Structural Query Language allows expert users to richly represent its information needs but unfortunately, the complexity of SQLs make them impractical in the Web search engines. Automatically detecting the concepts in an unstructured user's information need and generating a richly structured, multilingual equivalent query is an ideal solution. We utilize Wikipedia as a great concept repository and also some state of the art algorithms for extracting Wikipedia's concepts from the user's information need. This process is called "Query Wikification". Our experiments on the TEL corpus at CLEF2009 achieves +23% and +17% improvement in Mean Average Precision and Recall against the baseline. Our approach is unique in that, it does improve both precision and recall; two pans that often improving one, hurt the another.