Ingredient Extraction from Text in the Recipe Domain

04/18/2022
by   Arkin Dharawat, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/14/2022

Punctuation restoration in Swedish through fine-tuned KB-BERT

Presented here is a method for automatic punctuation restoration in Swed...
research
06/27/2023

Investigating Cross-Domain Behaviors of BERT in Review Understanding

Review score prediction requires review text understanding, a critical r...
research
12/07/2022

A Study on Extracting Named Entities from Fine-tuned vs. Differentially Private Fine-tuned BERT Models

Privacy preserving deep learning is an emerging field in machine learnin...
research
11/30/2021

Automatic Extraction of Medication Names in Tweets as Named Entity Recognition

Social media posts contain potentially valuable information about medica...
research
09/18/2023

AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification

Key information extraction involves recognizing and extracting text from...
research
08/21/2020

Fine-tune BERT for E-commerce Non-Default Search Ranking

The quality of non-default ranking on e-commerce platforms, such as base...

Please sign up or login with your details

Forgot password? Click here to reset