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Practical considerations when shifting to using deep learning for your text data

This will be presented in English.

Emmanuel Ameisen (Insight Data Science)
13:1013:50 Thursday, April 12, 2018
Secondary topics:  自然语言与语音技术(Natural Language and Speech Technologies)

您将学到什么 (What you'll learn)

  • Understand how to quickly prototype ways to understand and leverage your text data

描述 (Description)

Most companies in industry collect and leverage text data for some part of their business operations. Some, such as Yelp and Twitter, have text data at the core of their platform while most others utilize it behind the scenes, triaging and responding to support requests and customer feedback. Top companies have achieved incredible performance by switching to deep learning methods for text analysis. Companies making this shift, though, typically encounter a set of challenges which include determining which models to spend their time and money on, how to validate and explain model performance, and how model complexity affects the ease of deploying them. Examples of such business challenges include:

  • How do you automatically make the distinction between different categories of sentences?
  • How can you find sentences in a dataset that are most similar to a given one?
  • How can you extract a rich and concise representation that can then be used for a range of other tasks?
  • Most importantly, how do you find quickly whether these tasks are feasible on your dataset at all?

Drawing on research gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others, Emmanuel Ameisen and Jeremy Karnowski share a guide for moving your company from traditional machine learning approaches, such as logistic regression on bag-of-words features to more expressive deep learning models, such as convolutional neural networks and recurrent neural networks. These new techniques allow companies to improve many of the core algorithmic concerns that underlie a majority of key business operations, such as clustering (e.g., to identify topics in articles) and classification (e.g., to automatically forward support requests to the appropriate person). You’ll learn the trade-offs of different models in terms of power, complexity, and interpretability and understand how to choose the ones most appropriate for your projects.

Photo of Emmanuel Ameisen

Emmanuel Ameisen

Insight Data Science

Emmanuel Ameisen is an AI program director and machine learning engineer at Insight. Emmanuel has years of experience going from product ideation to effective implementations. Previously, he implemented and scaled out predictive analytics and machine learning solutions for Local Motion and Zipcar. Emmanuel holds master’s degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.