Summaries and summary-based query expansion A Study on the Use of Summaries and Summary-based Query Expansion for a Question-answering Task Ian Ruthven, Anastasios Tombros and Joemon M. Jose Department of Computing Scie

Source URL: www.dcs.gla.ac.uk

• Relevance
• Query expansion
• Full text search
• Question answering
• Human–computer information retrieval
• Information science
• Information retrieval
• Relevance feedback

TREC 2003 Question Answering Track at CAS-ICT Yi Chang, Hongbo Xu, Shuo Bai Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China {changyi, hbxu}@software.ict.ac.cn Abstract In our system, we mak

Source URL: trec.nist.gov

• Computational linguistics
• Information retrieval
• Natural language processing
• Question answering
• Text Retrieval Conference
• Sentence
• Question
• Fact
• Science
• Information science
• Linguistics

Coupling Named Entity Recognition, Vector-Space Model and Knowledge Bases for TREC-11 Question Answering Track P. Bellot(1), E. Crestan(1,2), M. El-Bèze (1), L. Gillard(1), C. de LoupyLaboratoire d’Informatiqu

Source URL: trec.nist.gov

• Linguistics
• Information retrieval
• Natural language processing
• Grammar
• Human communication
• Question answering
• K
• Question
• Yes and no
• Computational linguistics
• Science
• Information science

IBM’s PIQUANT in TREC2003

Source URL: trec.nist.gov

• Information retrieval
• Natural language processing
• Artificial intelligence applications
• Artificial intelligence
• Question answering
• Human communication
• QA
• Question
• Cyc
• Science
• Information science
• Computational linguistics

Combining Lexicon Expansion, Information Retrieval, and Cluster-based Ranking for Biomedical Question Answering A. M. Cohen1, J. Yang1, S. Fisher2, B. Roark2, and W.R. Hersh1 Department of Medical Informatics and Clinica

Source URL: trec.nist.gov

Microsoft Word - trec-2007-ciqa.doc

Source URL: trec.nist.gov

• Natural language processing
• Information
• Computational linguistics
• Artificial intelligence applications
• Relevance feedback
• Question answering
• Question
• Yes and no
• Precision and recall
• Science
• Information science
• Information retrieval

A Neural Network for Factoid Question Answering over Paragraphs Mohit Iyyer1 , Jordan Boyd-Graber2 , Leonardo Claudino1 , Richard Socher3 , Hal Daum´ e III1 1

Source URL: cs.umd.edu

• Natural language processing
• Speech recognition
• Grammar
• Human communication
• Question
• Yes and no
• Quiz bowl
• Sentence
• N-gram
• Linguistics
• Computational linguistics
• Science

TREC2007 QUESTION ANSWERING EXPERIMENTS AT TOKYO INSTITUTE OF TECHNOLOGY Edward W. D. Whittaker, Matthias H. Heie, Josef R. Novak and Sadaoki Furui Tokyo Institute of Technology, Department of Computer ScienceOok

Source URL: trec.nist.gov

• Information science
• Information retrieval
• Question answering
• Language model
• Document retrieval
• Sentence extraction
• Question
• N-gram
• Natural language processing
• Science
• Computational linguistics

External Knowledge Sources for Question Answering Boris Katz, Gregory Marton, Gary Borchardt, Alexis Brownell, Sue Felshin, Daniel Loreto, Jesse Louis-Rosenberg, Ben Lu, ¨ Federico Mora, Stephan Stiller, Ozlem Uzuner, A

Source URL: trec.nist.gov

• Information
• Question answering
• Precision and recall
• Mean reciprocal rank
• Tf*idf
• Wikipedia
• Document retrieval
• Yahoo! Answers
• Information science
• Information retrieval
• Science

In Proceedings of the 7th International Workshop on Applications of Natural Language to Information Systems (NLDB 2002), June 2002, Stockholm, Sweden Omnibase: Uniform Access to Heterogeneous Data for Question Answering

Source URL: start.csail.mit.edu

• Database management systems
• Data management
• Artificial intelligence applications
• Information retrieval
• Natural language processing
• Data integration
• Question answering
• Database
• Wrapper
• Information science
• Science
• Information