基于顺序结构食谱的菜系分类
Classification of Cuisines from Sequentially Structured Recipes
摘要：
世界各地的文化因其烹饪的独特模式而与众不同。这些菜肴的特点是其子结构，如成分，烹饪过程和用具。一个地区固有的这些子结构的复杂融合定义了美食的身份。基于烹饪特征对烹饪进行准确分类是一个突出的问题，迄今为止一直试图通过将食谱的成分作为特征来解决。以前的研究试图通过使用非结构化的食谱，而不考虑烹饪技术的细节美食分类。实际上，烹饪过程/技术及其顺序对于食谱的结构及其分类非常重要。在本文中，我们已经实现了一系列的分类技术，通过在包含食谱顺序数据的RecipeDB数据集上解释这些信息。在从逻辑回归和朴素贝叶斯到LSTM和Transformers的一系列分类模型中，最先进的RoBERTa模型的准确率最高，为73.30%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
