Papers by Ganesh Bagler

2 papers
Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)

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Challenge: Named entity recognition is a technique for extracting information from unstructured data with known labels.
Approach: They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases .
Outcome: The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods .
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)

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Challenge: Named entities pose a unique challenge to traditional methods of language modeling.
Approach: They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources.
Outcome: The proposed model is based on 158,473 cooking recipes from public sources.

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