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- W2940193568 abstract "Internet era witnesses information explosion in which it seems particularly vital to locate the necessary information quickly. Most of the web information is in the form of texts. For this purpose, we study the fairness and performance of the text retrieval models in order to improve the performance in the actual practice. All the experiments were conducted on Lemur information retrieval system, which includes powerful toolkit to evaluate retrieval models. This paper makes a comparative analysis of four kinds of retrieval models: language model, BM25, TF-IDF and DFI model in accuracy and fairness to find out the retrieval models that make better performance in both two aspects. Then, we optimize the parameters of the language model and BM25 model. Furthermore, we improve the DFI model by changing the method of the distance measurement, which enhances the performance of the model to a certain extent." @default.
- W2940193568 created "2019-04-25" @default.
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- W2940193568 date "2018-07-01" @default.
- W2940193568 modified "2023-09-27" @default.
- W2940193568 title "Exploring Fairness and Accuracy of Retrieval Models" @default.
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- W2940193568 doi "https://doi.org/10.1109/fskd.2018.8687122" @default.
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