Selecting effective terms for query formulation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5839 LNCS, Issue , 2009, Pages 168-180

  Lee, Chia Jung ^a   Lin, Yi Chun ^a   Chen, Ruey Cheng ^a   Cheng, Pu Jen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

DOCUMENT COLLECTION; QUERY FORMULATION; QUERY TERMS; RETRIEVAL PERFORMANCE; STATISTICAL PROPERTIES;

INFORMATION SERVICES; INFRARED DEVICES; INFRARED INSTRUMENTS; SOUNDING APPARATUS;

INFORMATION RETRIEVAL;

EID: 70549098908     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04769-5_15     Document Type: Conference Paper

References (21)

1. Allan, J., Callan, J., Croft, W.B., Ballesteros, L., Broglio, J., Xu, J., Shu, H.: INQUERY at TREC-5. In: Fifth Text REtrieval Conference (TREC-5), pp. 119-132 (1997)
2. Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127-137. Springer, Heidelberg (2004)
3. Bendersky, M., Croft, W.B.: Discovering key concepts in verbose queries. In: 31st annual international ACM SIGIR, pp. 491-498 (2008)
4. Cao, G., Nie, J.Y., Gao, J.F., Robertson, S.: Selecting good expansion terms for pseudorelevance feedback. In: 31st annual international ACM SIGIR, pp. 243-250 (2008)
5. Carmel, D., Yom-Tov, E., Soboroff, I.: SIGIR Workshop Report: Predicting Query Difficulty - Methods and Applications. In: Workshop Session: SIGIR, pp. 25-28 (2005)
6. Carmel, D., Yom-Tov, E., Darlow, A., Pelleg, D.: What makes a query difficult? In: 29th annual international ACM SIGIR, pp. 390-397 (2006)
7. Carmel, D., Farchi, E., Petruschka, Y., Soffer, A.: Automatic query refinement using lexical affinities with maximal information gain. In: 25th annual international ACM SIGIR, pp. 283-290 (2002)
8. Chang, C.C., Lin, C.J.: LIBSVM (2001), http://www.csie.ntu.edu.tw/~cjlin/ libsvm
9. He, B., Ounis, I.: Inferring query performance using pre-retrieval predictors. In: 11th International Conference of String Processing and Information Retrieval, pp. 43-54 (2004)
10. Jones, R., Fain, D.C.: Query word deletion prediction. In: 26th annual international ACM SIGIR, pp. 435-436 (2003)
11. Kumaran, G., Allan, J.: Effective and efficient user interaction for long queries. In: 31st annual international ACM SIGIR, pp. 11-18 (2008)
12. Kumaran, G., Allan, J.: Adapting information retrieval systems to user queries. In: Information Processing and Management, pp. 1838-1862 (2008)
13. Kwok, K.L.: A new method of weighting query terms for ad-hoc retrieval. In: 19th annual international ACM SIGIR, pp. 187-195 (1996)
14. Lioma, C., Ounis, I.: Examining the content load of part of speech blocks for information retrieval. In: COLING/ACL 2006 Main Conference Poster Sessions (2006)
15. Mandl, T., Womser-Hacker, C.: Linguistic and statistical analysis of the CLEF topics. In: Third Workshop of the Cross-Language Evaluation Forum CLEF (2002)
16. Mothe, J., Tanguy, L.: ACM SIGIR 2005 Workshop on Predicting Query Difficulty - Methods and Applications (2005)
17. Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
18. Yom-Tov, E., Fine, S., Carmel, D., Darlow, A., Amitay, E.: Juru at TREC 2004: Experiments with prediction of query difficulty. In: 13th Text Retrieval Conference (2004)
19. Zhou, Y., Croft, W.B.: Query performance prediction in Web search environments. In: 30th Annual International ACM SIGIR Conference, pp. 543-550 (2007)
20. Zhou, Y., Croft, W.B.: Ranking Robustness: A novel framework to predict query performance. In: 15th ACM international conference on Information and knowledge management, pp. 567-574 (2006)
21. The Lemur Toolkit: http://www.lemurproject.org/