Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning
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.