Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Document ranking based upon Markov chains
Information Processing and Management: an International Journal
Sparse Distributed Memory
Information Retrieval
An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Orthogonal negation in vector spaces for modelling word-meanings and document retrieval
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
A study of methods for negative relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Re-ranking search results using language models of query-specific clusters
Information Retrieval
From "Identical" to "Similar": Fusing Retrieved Lists Based on Inter-document Similarities
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
From fusion to re-ranking: a semantic approach
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
CLEF 2009 ad hoc track overview: robust-WSD task
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
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Information about top-ranked documents plays a key role to improve retrieval performance. One of the most common strategies which exploits this kind of information is relevance feedback. Few works have investigated the role of negative feedback on retrieval performance. This is probably due to the difficulty of dealing with the concept of nonrelevant document. This paper proposes a novel approach to document re-ranking, which relies on the concept of negative feedback represented by non-relevant documents. In our model the concept of non-relevance is defined as a quantum operator in both the classical Vector Space Model and a Semantic Document Space. The latter is induced from the original document space using a distributional approach based on Random Indexing. The evaluation carried out on a standard document collection shows the effectiveness of the proposed approach and opens new perspectives to address the problem of quantifying the concept of non-relevance.