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Towards automated recipe genre classification using semi-supervised learning.
Nazmus Sakib1, G M Shahariar2, Md Mohsinul Kabir1
1SSL Lab, Dept. of CSE, Islamic University of Technology, Dhaka, Bangladesh.
Plos One
|January 28, 2025
Summary
A new dataset of two million cooking recipes, the Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) dataset, aids in recipe classification and named entity recognition (NER). This dataset achieved 98.6% accuracy in genre classification, highlighting the importance of recipe titles.
Area of Science:
- Natural Language Processing
- Machine Learning
- Computational Linguistics
Background:
- Categorizing online recipes into genres is difficult due to insufficient labeled data.
- Existing datasets lack comprehensive annotations for recipe analysis.
Purpose of the Study:
- Introduce the Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) Cooking Recipe Dataset.
- Provide a robust dataset for recipe classification, named entity recognition (NER), and generation tasks.
- Enhance NER capabilities by extending entity lists for recipe directions.
Main Methods:
- Developed the 3A2M+ dataset with over two million labeled recipes.
- Utilized two NER extraction tools to extend entity recognition for recipe directions.
- Applied traditional machine learning, deep learning, and pre-trained language models for genre classification.
Main Results:
- Achieved 98.6% overall accuracy in classifying recipes into nine genres.
- Demonstrated the effectiveness of the 3A2M+ dataset for various NLP tasks.
- Identified recipe titles as the most significant feature for genre classification.
Conclusions:
- The 3A2M+ dataset offers a comprehensive solution for recipe-related NLP challenges.
- The proposed NER extension pipeline improves the analysis of recipe instructions.
- Recipe title analysis is crucial for accurate genre classification.
