Query term ranking based on dependency parsing of verbose queries

SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2010, Pages 829-830

  Park, Jae Hyun ^a   Croft, W Bruce ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Dependency parse; Query reformulation; Query term ranking

Indexed keywords

DEPENDENCY PARSE; DEPENDENCY PARSING; QUERY REFORMULATION; QUERY TERMS; RANKING AND SELECTION; RANKING APPROACH; SYNTACTIC FEATURES;

INFORMATION RETRIEVAL;

FEATURE EXTRACTION;

EID: 77956054659     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835449.1835637     Document Type: Conference Paper

References (6)

1. M. Bendersky and W. B. Croft. Discovering key concepts in verbose queries. In Proc. ACM SIGIR, pages 491-498, 2008.
2. A. Chanen. A comparison of human and computationally generated document features for automatic text classification. PhD thesis, The University of Sydney, 2009.
3. M. De Marneffe, B. MacCartney, and C. Manning. Generating typed dependency parses from phrase structure parses. In Proc. LREC 2006, 2006.
4. T. Joachims. Optimizing Search Engines Using Clickthrough Data. In Proc. ACM KDD, pages 133-142, 2002.
5. G. Kumaran and V. Carvalho. Reducing long queries using query quality predictors. In Proc. ACM SIGIR, pages 564-571, 2009.
6. C. Lee, R. Chen, S. Kao, and P. Cheng. A term dependency-based approach for query terms ranking. In Proc. CIKM, pages 1267-1276, 2009.