Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
A comparison of statistical significance tests for information retrieval evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
NUS-PT: exploiting parallel texts for word sense disambiguation in the English all-words tasks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UBC-ALM: combining k-NN with SVD for WSD
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
CLEF 2008: ad hoc track overview
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
UniNE at CLEF 2008: TEL, and Persian IR
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
UNIBA-SENSE @ CLEF 2009: robust WSD task
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Negation for document re-ranking in ad-hoc retrieval
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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A number of works have shown that the aggregation of several Information Retrieval (IR) systems works better than each system working individually. Nevertheless, early investigation in the context of CLEF Robust-WSD task, in which semantics is involved, showed that aggregation strategies achieve only slight improvements. This paper proposes a re-ranking approach which relies on inter-document similarities. The novelty of our idea is twofold: the output of a semantic based IR system is exploited to re-weigh documents and a new strategy based on Semantic Vectors is used to compute inter-document similarities.