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Data Augmentation for Sequence Labeling. A Case Study in Food Parsing

Deep Learning supervised models are ubiquitously sought in the tech industry, where data and classification use cases abound.

While established tech companies have access to enough resources and data to employ state-of-the-art, transformer-based deep neural models, start-ups who struggle with data acquisition are forced to rely on classical machine learning models for their data processing needs.

In this talk, I will be discussing data augmentation techniques that I used in my NLP project for Lark, an acclaimed AI healthcare start-up, to enable their virtual nurse app to unlock the power of deep learning in a use case that is often overlooked in the industry: sequence labeling.

TLDR: Extreme Summarization of Scientific Documents

Scientists are often required to process long lists of papers, such as conference proceedings and search engine results, and information overload is...