Ingredient Extraction from Text in the Recipe Domain
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Abstract (arXiv)
In recent years, there has been an increase in the number of devices with virtual assistants (e.g: Siri, Google Home, Alexa) in our living rooms and kitchens. As a result of this, these devices receive several queries about recipes. All these queries will contain terms relating to a "recipe-domain" i.e: they will contain dish-names, ingredients, cooking times, dietary preferences etc. Extracting these recipe-relevant aspects from the query thus becomes important when it comes to addressing the user's information need. Our project focuses on extracting ingredients from such plain-text user utterances. Our best performing model was a fine-tuned BERT which achieved an F1-score of 95.01. We have released all our code in a GitHub repository.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 18 Apr 2022.
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References · 6
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
- 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
- 2019Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noisecited by 1
- 2019Extracting clinical concepts from user queriescited by 1
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