Title: The mining method of trigger word for food nutrition matching

Authors: Guangli Zhu; Qiaoyun Wang; Hanran Liu; Shunxiang Zhang

Addresses: School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China

Abstract: The trigger words related to food nutrition matching have an effect on classifying the rational food nutrition matching. This paper proposes a trigger word localisation method for food nutrition matching. First, food information frequency vector can be extracted by the number of food names, the number of nutrition ingredients and the number of matching effects in the sentence. The sentences unrelated to food nutrition matching can be filtered. Then, the two food verb-noun joint probability matrices can be constructed. By comparing row mean value of the two matrices, whether the verb is a trigger word can be judged. Lastly, under the premise of commendatory and derogatory probabilities of the trigger word, the food nutrition matching can be classified as two types by Naive Bayes. The experiments show that proposed method in this paper effectively detects the trigger word related to food nutrition matching.

Keywords: food nutrition matching; food information frequency vector; food verb-noun joint probability matrix.

DOI: 10.1504/IJCSE.2020.111423

International Journal of Computational Science and Engineering, 2020 Vol.23 No.3, pp.205 - 213

Received: 01 Aug 2019
Accepted: 20 Nov 2019

Published online: 24 Nov 2020 *

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