Enhancing Food Ingredient Named-Entity Recognition with Recurrent Network-Based Ensemble (RNE) Model

Abstract

Food recipe sharing sites are becoming increasingly popular among people who want to learn how to cook or plan their menu. Through online food recipes, individuals can select ingredients that suit their lifestyle and health condition. Information from online food recipes is useful in developing food-related systems such as recommendations and health care systems. However, the information from online recipes is often unstructured. One way of extracting such information into a well-structured format is the technique called named-entity recognition (NER), which is the process of identifying keywords and phrases in the text and classifying them into a set of predetermined categories, such as location, persons, time, and others. We present a food ingredient named-entity recognition model called RNE (recurrent network-based ensemble methods) to extract the entities from the online recipe. RNE is an ensemble-learning framework using recurrent network models such as RNN, GRU, and LSTM. These models are trained independently on the same dataset and combined to produce better predictions in extracting food entities such as ingredient names, products, units, quantities, and states for each ingredient in a recipe. The experimental findings demonstrate that the proposed model achieves predictions with an F1 score of 96.09% and outperforms all individual models by 0.2% to 0.5% in percentage points. This result indicates that RNE can extract information from food recipes better than a single model. In addition, this information extracted by RNE can be used to support various information systems related to food.

Keywords

• deep learning
• ensemble method
• food information extraction
• named-entity recognition
• recurrent models