Analyse des critères d'évaluation des systèmes de recherche d'information

Baccini, Alain; Déjean, Sébastien; Kompaoré, Désiré; Mothe, Josiane

doi:10.3166/tsi.29.289-308

Cited by 3 publications

(1 citation statement)

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“…The same library is then used for retrieving the top 1,000 documents, for each query using the TF-IDF of the vector space model [24]. The following metrics are used for evaluating the information retrieval performance of the proposed approach by comparing the responses of a system according to a query with a relevance judgement [25]: a. Precision: measures the proportion of relevant documents among all documents retrieved by the system.…”

Section: Test Collection and Evaluation Metricsmentioning

confidence: 99%

A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining

Jabri¹,

Dahbi²,

Gadi³

2019

IJECE

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Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query. However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and other terms are detected, and an oriented and weighted graph called Pseudo-Graph Feedback is constructed. This graph serves for expanding original queries by terms related semantically and selected by the user. The results of the experiments on Text Retrieval Conference (TREC) collection are very significant, and best results are achieved by the proposed approach compared to both the baseline system and an existing technique.

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Section: Test Collection and Evaluation Metricsmentioning

confidence: 99%