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Open AccessArticle

Fuzzy Information Retrieval Based on Continuous Bag-of-Words Model

College of Science, Chongqing University of Post and Telecommunication, Chongqing 400065, China
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Symmetry 2020, 12(2), 225; https://doi.org/10.3390/sym12020225
Received: 25 December 2019 / Revised: 13 January 2020 / Accepted: 21 January 2020 / Published: 3 February 2020
In this paper, we study the feasibility of performing fuzzy information retrieval by word embedding. We propose a fuzzy information retrieval approach to capture the relationships between words and query language, which combines some techniques of deep learning and fuzzy set theory. We try to leverage large scale data and the continuous-bag-of words model to find the relevant feature of words and obtain word embedding. To enhance retrieval effectiveness, we measure the relativity among words by word embedding, with the property of symmetry. Experimental results show that the recall ratio, precision ratio, and harmonic average of two ratios of the proposed method outperforms the ones of the traditional methods. View Full-Text
Keywords: word embedding; fuzzy information retrieval; continuous bag-of-words model; word similarity word embedding; fuzzy information retrieval; continuous bag-of-words model; word similarity
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Qiu, D.; Jiang, H.; Chen, S. Fuzzy Information Retrieval Based on Continuous Bag-of-Words Model. Symmetry 2020, 12, 225.

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