Ingredient Extraction from Text in the Recipe Domain
This work addresses the need for accurate ingredient extraction to improve virtual assistant responses for users seeking recipe information, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of extracting ingredients from plain-text user queries in the recipe domain, achieving an F1-score of 95.01 with a fine-tuned BERT model.
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.