论文标题
从顺序结构化食谱中分类美食
Classification of Cuisines from Sequentially Structured Recipes
论文作者
论文摘要
世界各地的文化都以美食中的特质模式来区分。这些美食的特征是它们的子结构（例如成分，烹饪过程和餐具）。这些区域固有的这些子结构的复杂融合定义了美食的身份。基于其烹饪特征的美食的准确分类是一个重大的问题，迄今已尝试通过考虑食谱的成分作为特征来解决。先前的研究尝试通过使用非结构化食谱而不考虑烹饪技术的细节来尝试分类。实际上，烹饪过程/技术及其顺序对于食谱的结构以及其分类非常重要。在本文中，我们通过在配方数据集上考虑了该信息，该信息包含配方的顺序数据，从而实现了一系列分类技术。最先进的罗伯塔模型在一系列分类模型中，从逻辑回归和幼稚的贝叶斯到LSTMS和变形金刚的分类模型中呈现出73.30％的最高精度。
Cultures across the world are distinguished by the idiosyncratic patterns in their cuisines. These cuisines are characterized in terms of their substructures such as ingredients, cooking processes and utensils. A complex fusion of these substructures intrinsic to a region defines the identity of a cuisine. Accurate classification of cuisines based on their culinary features is an outstanding problem and has hitherto been attempted to solve by accounting for ingredients of a recipe as features. Previous studies have attempted cuisine classification by using unstructured recipes without accounting for details of cooking techniques. In reality, the cooking processes/techniques and their order are highly significant for the recipe's structure and hence for its classification. In this article, we have implemented a range of classification techniques by accounting for this information on the RecipeDB dataset containing sequential data on recipes. The state-of-the-art RoBERTa model presented the highest accuracy of 73.30% among a range of classification models from Logistic Regression and Naive Bayes to LSTMs and Transformers.
