关键词: 食品安全,歧义消解,隐含马尔可夫模型,TF-IDF,支持向量机

Abstract: The article aimed to put forward a disambiguation algorithm which can correctly classify the unknown terms,based on the food safety information in network.The disambiguation algorithms used in this paper combines the hidden Markov model(HMM) and SVM classifier to achieve terminology disambiguation,based on the improved TF-IDF feature selection algorithm.This paper proposed a new feature extraction algorithm LN-TF-IDF with two additional weighting factors on traditional TF-IDF.Experiments show that,the improved TF-IDF disambiguation algorithm designed in the field of food safety enhances the effect of disambiguation by average 7.31% on the 202831 texts.It was compared with the traditional TF-IDF text feature selection algorithm,with the F-measure as evaluation criteria.At the same time,the effect of the algorithm is relatively stable on different experimental data sets obtained from different time.

Key words: Food safety,Disambiguation,HMM,TF-IDF,SVM