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  1. Crestani, F.; Rijsbergen, C.J. van: Information retrieval by imaging (1996) 0.01 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  2. Leininger, K.: Interindexer consistency in PsychINFO (2000) 0.01 
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    Date
    9. 2.1997 18:44:22
  3. Keenan, S.; Smeaton, A.F.; Keogh, G.: ¬The effect of pool depth on system evaluation in TREC (2001) 0.01 
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  4. Vegt, A. van der; Zuccon, G.; Koopman, B.: Do better search engines really equate to better clinical decisions? : If not, why not? (2021) 0.01 
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  5. Belkin, N.J.: ¬An overview of results from Rutgers' investigations of interactive information retrieval (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  6. King, D.W.: Blazing new trails : in celebration of an audacious career (2000) 0.01 
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    Date
    22. 9.1997 19:16:05
  7. Petrelli, D.: On the role of user-centred evaluation in the advancement of interactive information retrieval (2008) 0.01 
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    Source
    Information processing and management. 44(2008) no.1, S.22-38
  8. Pal, S.; Mitra, M.; Kamps, J.: Evaluation effort, reliability and reusability in XML retrieval (2011) 0.01 
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    Date
    22. 1.2011 14:20:56
  9. Chu, H.: Factors affecting relevance judgment : a report from TREC Legal track (2011) 0.01 
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    Date
    12. 7.2011 18:29:22
  10. Wildemuth, B.; Freund, L.; Toms, E.G.: Untangling search task complexity and difficulty in the context of interactive information retrieval studies (2014) 0.01 
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    Date
    6. 4.2015 19:31:22
  11. Ravana, S.D.; Taheri, M.S.; Rajagopal, P.: Document-based approach to improve the accuracy of pairwise comparison in evaluating information retrieval systems (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
  12. Rajagopal, P.; Ravana, S.D.; Koh, Y.S.; Balakrishnan, V.: Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment (2019) 0.01 
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    Date
    20. 1.2015 18:30:22
  13. Lioma, C.; Ounis, I.: ¬A syntactically-based query reformulation technique for information retrieval (2008) 0.01 
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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.
  14. Cross-language information retrieval (1998) 0.01 
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    Content
    Enthält die Beiträge: GREFENSTETTE, G.: The Problem of Cross-Language Information Retrieval; DAVIS, M.W.: On the Effective Use of Large Parallel Corpora in Cross-Language Text Retrieval; BALLESTEROS, L. u. W.B. CROFT: Statistical Methods for Cross-Language Information Retrieval; Distributed Cross-Lingual Information Retrieval; Automatic Cross-Language Information Retrieval Using Latent Semantic Indexing; EVANS, D.A. u.a.: Mapping Vocabularies Using Latent Semantics; PICCHI, E. u. C. PETERS: Cross-Language Information Retrieval: A System for Comparable Corpus Querying; YAMABANA, K. u.a.: A Language Conversion Front-End for Cross-Language Information Retrieval; GACHOT, D.A. u.a.: The Systran NLP Browser: An Application of Machine Translation Technology in Cross-Language Information Retrieval; HULL, D.: A Weighted Boolean Model for Cross-Language Text Retrieval; SHERIDAN, P. u.a. Building a Large Multilingual Test Collection from Comparable News Documents; OARD; D.W. u. B.J. DORR: Evaluating Cross-Language Text Filtering Effectiveness
    Editor
    Grefenstette, G.
  15. Larsen, B.; Ingwersen, P.; Lund, B.: Data fusion according to the principle of polyrepresentation (2009) 0.01 
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    Date
    22. 3.2009 18:48:28

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