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Voorhees, E.M.; Harman, D.: Overview of the Sixth Text REtrieval Conference (TREC-6) (2000)
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- Date
- 11. 8.2001 16:22:19
-
Petrelli, D.: On the role of user-centred evaluation in the advancement of interactive information retrieval (2008)
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- Source
- Information processing and management. 44(2008) no.1, S.22-38
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Leininger, K.: Interindexer consistency in PsychINFO (2000)
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- Date
- 9. 2.1997 18:44:22
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King, D.W.: Blazing new trails : in celebration of an audacious career (2000)
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- Date
- 22. 9.1997 19:16:05
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Rao, A.; Lu, A.; Meier, E.; Ahmed, S.; Pliske, D.: Query processing in TREC6 (2000)
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Larsen, B.; Ingwersen, P.; Lund, B.: Data fusion according to the principle of polyrepresentation (2009)
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- Date
- 22. 3.2009 18:48:28
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Hawking, D.; Craswell, N.: ¬The very large collection and Web tracks (2005)
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Hiemstra, D.; Kraaij, W.: ¬A language-modeling approach to TREC (2005)
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Amitay, E.; Carmel, D.; Lempel, R.; Soffer, A.: Scaling IR-system evaluation using Term Relevance Sets (2004)
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Brown, E.W.; Carmel, D.; Franz, M.; Ittycheriah, A.; Kanungo, T.; Maarek, Y.; McCarley, J.S.; Mack, R.L.; Prager, J.M.; Smith, J.R.; Soffer, A.; Zien, J.Y.; Marwick, A.D.: IBM research activities at TREC (2005)
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Robins, D.: Shifts of focus on various aspects of user information problems during interactive information retrieval (2000)
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Ruthven, I.; Baillie, M.; Elsweiler, D.: ¬The relative effects of knowledge, interest and confidence in assessing relevance (2007)
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Lioma, C.; Ounis, I.: ¬A syntactically-based query reformulation technique for information retrieval (2008)
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- Abstract
- Whereas in language words of high frequency are generally associated with low content [Bookstein, A., & Swanson, D. (1974). Probabilistic models for automatic indexing. Journal of the American Society of Information Science, 25(5), 312-318; Damerau, F. J. (1965). An experiment in automatic indexing. American Documentation, 16, 283-289; Harter, S. P. (1974). A probabilistic approach to automatic keyword indexing. PhD thesis, University of Chicago; Sparck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11-21; Yu, C., & Salton, G. (1976). Precision weighting - an effective automatic indexing method. Journal of the Association for Computer Machinery (ACM), 23(1), 76-88], shallow syntactic fragments of high frequency generally correspond to lexical fragments of high content [Lioma, C., & Ounis, I. (2006). Examining the content load of part of speech blocks for information retrieval. In Proceedings of the international committee on computational linguistics and the association for computational linguistics (COLING/ACL 2006), Sydney, Australia]. We implement this finding to Information Retrieval, as follows. We present a novel automatic query reformulation technique, which is based on shallow syntactic evidence induced from various language samples, and used to enhance the performance of an Information Retrieval system. Firstly, we draw shallow syntactic evidence from language samples of varying size, and compare the effect of language sample size upon retrieval performance, when using our syntactically-based query reformulation (SQR) technique. Secondly, we compare SQR to a state-of-the-art probabilistic pseudo-relevance feedback technique. Additionally, we combine both techniques and evaluate their compatibility. We evaluate our proposed technique across two standard Text REtrieval Conference (TREC) English test collections, and three statistically different weighting models. Experimental results suggest that SQR markedly enhances retrieval performance, and is at least comparable to pseudo-relevance feedback. Notably, the combination of SQR and pseudo-relevance feedback further enhances retrieval performance considerably. These collective experimental results confirm the tenet that high frequency shallow syntactic fragments correspond to content-bearing lexical fragments.
-
TREC: experiment and evaluation in information retrieval (2005)
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- Content
- Enthält die Beiträge: 1. The Text REtrieval Conference - Ellen M. Voorhees and Donna K. Harman 2. The TREC Test Collections - Donna K. Harman 3. Retrieval System Evaluation - Chris Buckley and Ellen M. Voorhees 4. The TREC Ad Hoc Experiments - Donna K. Harman 5. Routing and Filtering - Stephen Robertson and Jamie Callan 6. The TREC Interactive Tracks: Putting the User into Search - Susan T. Dumais and Nicholas J. Belkin 7. Beyond English - Donna K. Harman 8. Retrieving Noisy Text - Ellen M. Voorhees and John S. Garofolo 9.The Very Large Collection and Web Tracks - David Hawking and Nick Craswell 10. Question Answering in TREC - Ellen M. Voorhees 11. The University of Massachusetts and a Dozen TRECs - James Allan, W. Bruce Croft and Jamie Callan 12. How Okapi Came to TREC - Stephen Robertson 13. The SMART Project at TREC - Chris Buckley 14. Ten Years of Ad Hoc Retrieval at TREC Using PIRCS - Kui-Lam Kwok 15. MultiText Experiments for TREC - Gordon V. Cormack, Charles L. A. Clarke, Christopher R. Palmer and Thomas R. Lynam 16. A Language-Modeling Approach to TREC - Djoerd Hiemstra and Wessel Kraaij 17. BM Research Activities at TREC - Eric W. Brown, David Carmel, Martin Franz, Abraham Ittycheriah, Tapas Kanungo, Yoelle Maarek, J. Scott McCarley, Robert L. Mack, John M. Prager, John R. Smith, Aya Soffer, Jason Y. Zien and Alan D. Marwick Epilogue: Metareflections on TREC - Karen Sparck Jones
