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Salton, G.; Allan, J.: Selective text utilization and text traversal (1995) 0.01

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Source: International journal of human-computer studies. 43(1995) no.3, S.xxx-xxx

Agosti, M.; Allan, J.: Introduction to the special issue on methods and tools for the automatic construction of hypertext (1997) 0.01

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Source: Information processing and management. 33(1997) no.2, S.129-131

Salton, G.; Allan, J.; Singhal, A.: Automatic text decomposition and structuring (1996) 0.01

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Source: Information processing and management. 32(1996) no.2, S.127-138

Allan, J.: Building hypertext using information retrieval (1997) 0.01

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Source: Information processing and management. 33(1997) no.2, S.145-159

Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995) 0.00

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Source: Information processing and management. 31(1995) no.3, S.315-326

Kumaran, G.; Allan, J.: Adapting information retrieval systems to user queries (2008) 0.00

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Source: Information processing and management. 44(2008) no.6, S.1838-1862

Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.00

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Date: 27. 2.1999 20:55:22

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.
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.

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