-
Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997)
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- Date
- 27. 2.1999 20:55:22
- Source
- The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
-
Allan, J.; Croft, W.B.; Callan, J.: ¬The University of Massachusetts and a dozen TRECs (2005)
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- Source
- TREC: experiment and evaluation in information retrieval. Ed.: E.M. Voorhees, u. D.K. Harman
-
Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.478-483.
-
Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022)
0.03
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- Abstract
- Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat-B collection.
-
Dang, E.K.F.; Luk, R.W.P.; Allan, J.; Ho, K.S.; Chung, K.F.L.; Lee, D.L.: ¬A new context-dependent term weight computed by boost and discount using relevance information (2010)
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- Abstract
- We studied the effectiveness of a new class of context-dependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the context-dependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the context-independent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections.
-
Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Salton, G.; Allan, J.: Selective text utilization and text traversal (1995)
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-
Allan, J.; Ballesteros, L.; Callan, J.P.; Croft, W.B.; Lu, Z.: Recent experiment with INQUERY (1996)
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-
Agosti, M.; Allan, J.: Introduction to the special issue on methods and tools for the automatic construction of hypertext (1997)
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-
Salton, G.; Buckley, C.; Allan, J.: Automatic structuring of text files (1992)
0.01
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-
Salton, G.; Allan, J.; Singhal, A.: Automatic text decomposition and structuring (1996)
0.01
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-
Allan, J.: Building hypertext using information retrieval (1997)
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-
Papka, R.; Allan, J.: Topic detection and tracking : event clustering as a basis for first story detection (2000)
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Kumaran, G.; Allan, J.: Adapting information retrieval systems to user queries (2008)
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Dang, E.K.F.; Luk, R.W.P.; Allan, J.: Beyond bag-of-words : bigram-enhanced context-dependent term weights (2014)
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Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A context-dependent relevance model (2016)
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