Deep Learning Based Named Entity Recognition Models for Recipes

• URL: http://arxiv.org/abs/2402.17447v2
• Date: Thu, 6 Jun 2024 07:41:21 GMT
• Title: Deep Learning Based Named Entity Recognition Models for Recipes
• Authors: Mansi Goel, Ayush Agarwal, Shubham Agrawal, Janak Kapuriya, Akhil Vamshi Konam, Rishabh Gupta, Shrey Rastogi, Niharika, Ganesh Bagler,
• Abstract summary: Named entity recognition (NER) is a technique for extracting information from unstructured or semi-structured data with known labels. We created an augmented dataset of 26,445 phrases cumulatively. We analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER. A thorough investigation of NER approaches on these datasets involving statistical, fine-tuning of deep learning-based language models provides deep insights.
• Score: 7.507956305171027
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Food touches our lives through various endeavors, including flavor, nourishment, health, and sustainability. Recipes are cultural capsules transmitted across generations via unstructured text. Automated protocols for recognizing named entities, the building blocks of recipe text, are of immense value for various applications ranging from information extraction to novel recipe generation. Named entity recognition is a technique for extracting information from unstructured or semi-structured data with known labels. Starting with manually-annotated data of 6,611 ingredient phrases, we created an augmented dataset of 26,445 phrases cumulatively. Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER. Based on the analysis, we sampled a subset of 88,526 phrases using a clustering-based approach while preserving the diversity to create the machine-annotated dataset. A thorough investigation of NER approaches on these three datasets involving statistical, fine-tuning of deep learning-based language models and few-shot prompting on large language models (LLMs) provides deep insights. We conclude that few-shot prompting on LLMs has abysmal performance, whereas the fine-tuned spaCy-transformer emerges as the best model with macro-F1 scores of 95.9%, 96.04%, and 95.71% for the manually-annotated, augmented, and machine-annotated datasets, respectively.

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