Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Automatic text decomposition using text segments and text themes
Proceedings of the the seventh ACM conference on Hypertext
Experimentation as a way of life: Okapi at TREC
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
Extended Boolean information retrieval
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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In this paper, we extended the vectorial model of Salton [9], [11], [12] and [14], by adapting the TF-IDF parameter by its combination with the Okapi formula for index terms extraction and evaluation of the in order to identify the relevant concepts which represent a document. Indeed, we have proposed a new measure TFIDF-ABR which takes in consideration the concept of semantic vicinity using a measure of similarity between terms by combining the calculation of TF-IDF-Okapi with a kernel approach (Radial Basis function). This indexation approach allows a contextual and semantic research. In order to have a robust descriptor index, we used not only a semantic graph to highlight the semantic connections between terms, but also an auxiliary dictionary to increase the connectivity of the constructed graph and therefore the semantic weight of indexation words.