Effective document presentation with a locality-based similarity heuristic
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Shortest-substring retrieval and ranking
ACM Transactions on Information Systems (TOIS)
A note about the proximity operators in information retrieval
SIGPLAN '73 Proceedings of the 1973 meeting on Programming languages and information retrieval
An information retrieval model using the fuzzy proximity degree of term occurences
Proceedings of the 2005 ACM symposium on Applied computing
Term proximity scoring for ad-hoc retrieval on very large text collections
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Lexical cohesion and term proximity in document ranking
Information Processing and Management: an International Journal
Term proximity scoring for keyword-based retrieval systems
ECIR'03 Proceedings of the 25th European conference on IR research
Viewing term proximity from a different perspective
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
ENSM-SE and UJM at INEX 2010: scoring with proximity and tag weights
INEX'10 Proceedings of the 9th international conference on Initiative for the evaluation of XML retrieval: comparative evaluation of focused retrieval
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We present in this paper some experiments on the Wikipedia collection used in the INEX 2009 evaluation campaign with an information retrieval method based on proximity. The idea of the method is to assign to each position in the document a fuzzy proximity value depending on its closeness to the surrounding keywords. These proximity values can then be summed on any range of text - including any passage or any element - and after normalization this sum is used as the relevance score for the extent. To take into account the semantic tags, we define a contextual operator which allow to consider at query time only the occurrences of terms that appear in a given semantic context.