基于主动学习的分类多样、具代表性且带标注的200万（3A2M）烹饪食谱数据集
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning
AI总结：
本研究针对网上食谱分类标注数据不足的问题，结合专家知识与主动学习技术，构建了含200万条带分类和置信度的3A2M烹饪食谱数据集，可支撑多项机器学习与NLP任务研究。
摘要：
烹饪食谱可供人们交流烹饪创意，并提供食物制作指导。由于缺乏充足的标注数据，将网上的原始食谱归类到合适的美食类别是该领域的一项挑战性任务。借助领域专家的知识对食谱进行分类或许是一种解决方案。本研究中，我们利用美食专家的知识与主动学习技术，提出了一个包含200万条烹饪食谱的新型数据集，所有食谱均标注了对应类别。\n为构建该数据集，我们从RecipeNLG数据集（一个食谱自然语言生成数据集）中收集食谱。随后，我们聘请了3位可信度得分高于86.667%的人类专家，通过命名实体识别（NER）对30万条食谱进行分类，将其归入9个类别之一：烘焙食品、饮品、荤食、蔬菜、快餐、谷物餐、正餐、配菜和融合料理。最后，我们结合委员会查询和人在回路（HITL）方法的主动学习方法，对剩余的190万条食谱进行分类。\n我们的数据集包含超过200万条食谱，每条都已分类并关联有置信度得分。对于这9个类别，该大规模数据集的Fleiss Kappa得分约为0.56026。我们认为研究界可使用该数据集开展各类机器学习任务，例如食谱类别分类、特定类别食谱生成、新食谱创作等。该数据集还可用于训练和评估各类NLP任务的性能，如命名实体识别、词性标注、语义角色标注等。数据集将于论文发表后开放获取：https://tinyurl.com/3zu4778y。
英文摘要：
Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique. To construct the dataset, we collect the recipes from the RecipeNLG dataset. Then, we employ three human experts whose trustworthiness score is higher than 86.667% to categorize 300K recipe by their Named Entity Recognition (NER) and assign it to one of the nine categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides and fusion. Finally, we categorize the remaining 1900K recipes using Active Learning method with a blend of Query-by-Committee and Human In The Loop (HITL) approaches. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it. For the 9 genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We believe that the research community can use this dataset to perform various machine learning tasks such as recipe genre classification, recipe generation of a specific genre, new recipe creation, etc. The dataset can also be used to train and evaluate the performance of various NLP tasks such as named entity recognition, part-of-speech tagging, semantic role labeling, and so on. The dataset will be available upon publication: https://tinyurl.com/3zu4778y.
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。
