A geometric view on bilingual lexicon extraction from comparable corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dimensionality reduction aids term co-occurrence based multi-document summarization
SumQA '06 Proceedings of the Workshop on Task-Focused Summarization and Question Answering
Comparison of similarity measures for clustering Turkish documents
Intelligent Data Analysis
Research on a novel word co-occurrence model and its application
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Lexical entailment for information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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The design of efficient textual similarities is an important issue in the domain of textual data exploration. Textual similarities are for example central in document collection structuring (e.g. clustering), or in Information Retrieval (IR) which relies on the computation of textual similarities for measuring the adequacy between a query and documents.The objective of this paper is to present and compare several textual similarity measures in the framework of the Distributional Semantics (DS) model for IR. This model is an extension of the standard Vector Space model, which further takes the co-frequencies between the terms in a given reference corpus into account. These co-frequencies are considered to provide a distributional representation of the "semantics" of the terms. The co-occurrence profiles are used to represent the documents as vectors.Practical retrieval experiments using DS-based similarity models have been conducted in the framework of the AMARYLLIS evaluation campaign. The results obtained are presented. They indicate significant improvement of the performance in comparison with the standard approach.