POS tagging-probability weighted method for matching the Internet recipe ingredients with food composition data

Abstract

In this paper, we present a new method that can be used for matching recipe ingredients extracted from the Internet to nutritional data from food composition databases (FCDBs). The method uses part of speech tagging (POS tagging) to capture the information from the names of the ingredients and the names of the food analyses from FCDBs. Then, probability weighted model is presented, which takes into account the information from POS tagging to assign the weight on each match and the match with the highest weight is used as the most relevant one and can be used for further analyses. We evaluated our method using a collection of 721 lunch recipes, from which we extracted 1,615 different ingredients and the result showed that our method can match 91.82% of the ingredients with the FCDB.