# Deep Learning Based Named Entity Recognition Models for Recipes

arXiv: https://arxiv.org/abs/2402.17447 · HTML: https://mlsanity.com/paper/2402.17447/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Lexicon Infused Phrase Embeddings for Named Entity Resolution](https://mlsanity.com/paper/1404.5367/) (2014)
- [Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble](https://mlsanity.com/paper/2212.06522/) (2022)
- [VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna](https://mlsanity.com/paper/2305.03253/) (2023)
- [Attending to Entities for Better Text Understanding](https://mlsanity.com/paper/1911.04361/) (2019)
- [A Named Entity Based Approach to Model Recipes](https://mlsanity.com/paper/2004.12184/) (2020)
- [Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques](https://mlsanity.com/paper/2301.00321/) (2023)

## Built on by (most cited first, 3 of 3)

- [CareLab at #SMM4H-HeaRD 2025: Insomnia Detection and Food Safety Event Extraction with Domain-Aware Transformers](https://mlsanity.com/paper/2506.18185/) (2025)
- [LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2603.01778/) (2026)
- [A framework for recipe data structure with applications for culinary and nutritional insights](https://mlsanity.com/paper/2609.22099/) (2026)

## Abstract (arXiv)

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.