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  • × author_ss:"Allan, J."
  1. Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.01 
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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
  2. 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) 0.00 
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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.
  3. Allan, J.; Ballesteros, L.; Callan, J.P.; Croft, W.B.; Lu, Z.: Recent experiment with INQUERY (1996) 0.00 
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  4. Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995) 0.00 
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    Abstract
    The Smart information retrieval project emphazises completely automatic approaches to the understanding and retrieval of large quantities of text. The work in the TREC-2 environment continues, performing both routing and ad hoc experiments. The ad hoc work extends investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document that matches the query. The performance of ad hoc runs is good, but it is clear that full advantage of the available local information is not been taken advantage of. The routing experiments use conventional relevance feedback approaches to routing, but with a much greater degree of query expansion than was previously done. The length of a query vector is increased by a factor of 5 to 10 by adding terms found in previously seen relevant documents. This approach improves effectiveness by 30-40% over the original query
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  5. Papka, R.; Allan, J.: Topic detection and tracking : event clustering as a basis for first story detection (2000) 0.00 
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    Abstract
    Topic Detection and Tracking (TDT) is a new research area that investigates the organization of information by event rather than by subject. In this paper, we provide an overview of the TDT research program from its inception to the third phrase that is now underway. We also discuss our approach to two of the TDT problems in detail. For event clustering (Detection), we show that classic Information Retrieval clustering techniques can be modified slightly to provide effective solutions. For first story detection, we show that similar methods provide satisfactory results, although substantial work remains. In both cases, we explore solutions that model the temporal relationship between news stories. We also investigate the use of phrase extraction to capture the who, what, when, and where contained in news
    Series
    The Kluwer international series on information retrieval; 7
    Source
    Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. Ed.: W.B. Croft
  6. Allan, J.; Croft, W.B.; Callan, J.: ¬The University of Massachusetts and a dozen TRECs (2005) 0.00 
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    Source
    TREC: experiment and evaluation in information retrieval. Ed.: E.M. Voorhees, u. D.K. Harman
  7. Allan, J.: Building hypertext using information retrieval (1997) 0.00 
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    Abstract
    Presents entirely automatic methods for gathering documents for a hypertext, linking the set, and annotating those connections with a description of the type of the link. Document linking is based upon information retrieval similarity measures with adjustable levels of strictness. Applies an approach inspired by relationship visualization techniques and by graph simplification, to show how to identify automatically tangential, revision, summary, expansion, comparisn, contrast, equivalence, and aggregate links
  8. Dang, E.K.F.; Luk, R.W.P.; Allan, J.: Beyond bag-of-words : bigram-enhanced context-dependent term weights (2014) 0.00 
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    Abstract
    While term independence is a widely held assumption in most of the established information retrieval approaches, it is clearly not true and various works in the past have investigated a relaxation of the assumption. One approach is to use n-grams in document representation instead of unigrams. However, the majority of early works on n-grams obtained only modest performance improvement. On the other hand, the use of information based on supporting terms or "contexts" of queries has been found to be promising. In particular, recent studies showed that using new context-dependent term weights improved the performance of relevance feedback (RF) retrieval compared with using traditional bag-of-words BM25 term weights. Calculation of the new term weights requires an estimation of the local probability of relevance of each query term occurrence. In previous studies, the estimation of this probability was based on unigrams that occur in the neighborhood of a query term. We explore an integration of the n-gram and context approaches by computing context-dependent term weights based on a mixture of unigrams and bigrams. Extensive experiments are performed using the title queries of the Text Retrieval Conference (TREC)-6, TREC-7, TREC-8, and TREC-2005 collections, for RF with relevance judgment of either the top 10 or top 20 documents of an initial retrieval. We identify some crucial elements needed in the use of bigrams in our methods, such as proper inverse document frequency (IDF) weighting of the bigrams and noise reduction by pruning bigrams with large document frequency values. We show that enhancing context-dependent term weights with bigrams is effective in further improving retrieval performance.
  9. Kumaran, G.; Allan, J.: Adapting information retrieval systems to user queries (2008) 0.00 
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    Abstract
    Users enter queries that are short as well as long. The aim of this work is to evaluate techniques that can enable information retrieval (IR) systems to automatically adapt to perform better on such queries. By adaptation we refer to (1) modifications to the queries via user interaction, and (2) detecting that the original query is not a good candidate for modification. We show that the former has the potential to improve mean average precision (MAP) of long and short queries by 40% and 30% respectively, and that simple user interaction can help towards this goal. We observed that after inspecting the options presented to them, users frequently did not select any. We present techniques in this paper to determine beforehand the utility of user interaction to avoid this waste of time and effort. We show that our techniques can provide IR systems with the ability to detect and avoid interaction for unpromising queries without a significant drop in overall performance.
    Footnote
    Beitrag in einem Themenheft "Adaptive information retrieval"
  10. Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994) 0.00 
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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.
  11. Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A context-dependent relevance model (2016) 0.00 
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    Abstract
    Numerous past studies have demonstrated the effectiveness of the relevance model (RM) for information retrieval (IR). This approach enables relevance or pseudo-relevance feedback to be incorporated within the language modeling framework of IR. In the traditional RM, the feedback information is used to improve the estimate of the query language model. In this article, we introduce an extension of RM in the setting of relevance feedback. Our method provides an additional way to incorporate feedback via the improvement of the document language models. Specifically, we make use of the context information of known relevant and nonrelevant documents to obtain weighted counts of query terms for estimating the document language models. The context information is based on the words (unigrams or bigrams) appearing within a text window centered on query terms. Experiments on several Text REtrieval Conference (TREC) collections show that our context-dependent relevance model can improve retrieval performance over the baseline RM. Together with previous studies within the BM25 framework, our current study demonstrates that the effectiveness of our method for using context information in IR is quite general and not limited to any specific retrieval model.
  12. Salton, G.; Buckley, C.; Allan, J.: Automatic structuring of text files (1992) 0.00 
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    Abstract
    In many practical information retrieval situations, it is necessary to process heterogeneous text databases that vary greatly in scope and coverage and deal with many different subjects. In such an environment it is important to provide flexible access to individual text pieces and to structure the collection so that related text elements are identified and properly linked. Describes methods for the automatic structuring of heterogeneous text collections and the construction of browsing tools and access procedures that facilitate collection use. Illustrates these emthods with searches using a large automated encyclopedia
  13. 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) 0.00 
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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.

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