Sep 9–12, 2019

Named Entity Recognition at scale with Deep Learning

Sijun He (Twitter)
4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 C

Who is this presentation for?

Software engineers, data scientists, and ML engineers




Twitter is what’s happening in the world right now and operating at such a global scale brings massive engineering challenges. To connect users with the best content, Twitter needs to build up a deep understanding of its text content. Such understanding needs to be

  • Scalable to annotate more than 500 million tweets per day
  • Real-time to accommodate the live nature of Twitter
  • Multilingual due to the number of languages Twitter support

Sijun He offers insights into how Twitter Cortex built and productionized a deep learning based NER system to address the above-mentioned challenges. We also highlight our experimentations with State-of-the-Art models (i.e. BERT) and learning methods (i.e. semi-supervised learning, active learning), as well as how we have balanced such efforts to keep in sync with recent developments in NLP with engineering needs.

Prerequisite knowledge

basic understanding of ML, NLP and Deep Learning

What you'll learn

* Learn how Twitter developed its NER system, from data sampling to labeling, from model development to serving infrastructure. * Understand the models for Named Entity Recognition (NER) and serving infrastructure needed to put those models into production
Photo of Sijun He

Sijun He


Sijun He is a machine learning engineer at Twitter Cortex, where he works on content understanding with Deep Learning and NLP. Previous he was a data scientist at Autodesk. Sijun holds a MS in Statistics from Stanford University.

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