Sep 23–26, 2019

Schedule: Text and Language processing and analysis sessions

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9:00am12:30pm Tuesday, September 24, 2019
Location: 1A 23/24
Alice Zhao (Metis)
As a data scientist, we are known to crunch numbers, but what happens when we run into text data? In this tutorial, I will walk through the steps to turn text data into a format that a machine can understand, share some of the most popular text analytics techniques, and showcase several natural language processing (NLP) libraries in Python including NLTK, TextBlob, spaCy and gensim. Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 23/24
David Talby (Pacific AI), Alex Thomas (Indeed), Saif Addin Ellafi (John Snow Labs)
This is a hands-on tutorial on state-of-the-art NLP using the highly performant, highly scalable open-source Spark NLP library. You'll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve. Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 12/14
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Saif Addin Ellafi (John Snow Labs), Scott Hoch (Deep6.ai)
Recruiting patients for clinical trials is a major challenge in drug development. This talk explains how Deep6 utilizes Spark NLP to scale its training and inference pipelines to millions of patients while achieving state-of-the-art accuracy. It covers the technical challenges, the architecture of the full solution, and lessons learned. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Panos Alexopoulos (Textkernel BV)
In an era where discussions among data scientists are monopolized by the latest trends in Machine Learning, the role of Semantics in Data Science is often underplayed. In this talk, I present real-world cases where making fine, seemingly pedantic, distinctions in the meaning of data science tasks and their related data, has helped improve significantly their effectiveness and value. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Gerard de Melo (Rutgers University)
What kinds of sentiment and emotions do consumers associate with a text? With new data-driven approaches, organizations can better pay attention to what is being said about them in different markets. We can also consider the fonts and color palettes best-suited to convey specific emotions, so that organizations can make informed choices when presenting information to consumers. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
John Berryman (Eventbrite)
Eventbrite is exploring a new machine learning approach that allows us to harvest data from customer search logs and automatically tag events based upon their content. The results have allowed us to provide users with a better inventory browsing experience. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 1E 12/13
Vlad Eidelman (FiscalNote)
While regulations affect your life every day, and millions of public comments are submitted to regulatory agencies in response to their proposals, analyzing the comments has traditionally been reserved for legal experts. In this talk, we show how natural language processing and machine learning can be used to automate the process by analyzing over 10 million publicly released comments. Read more.
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5:25pm6:05pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Sireesha Muppala (Amazon Web Services), Shelbee Eigenbrode (Amazon Web Services), Emily Webber (Amazon Web Services)
Mansplaining. Know it? Hate it? Want to make it go away? In this session we tackle the chronic problem of men talking over or down to women and its negative impact on career progression for women. We will also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds. We discuss ownership of the problem for both women and men, and suggest helpful strategies. Read more.
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1:15pm1:55pm Thursday, September 26, 2019
Location: 1A 12/14
Sandra Carrico (Glynt.ai)
This talk motivates mixed formal learning, explains it and outlines one machine learning example that previously used large numbers of examples and now learns with either zero or a handful of training examples. It maps apparently idiosyncratic techniques to Mixed Formal Learning, a general AI architecture that you can use in your projects. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1E 12/13
Madhu Gopinathan (MakeMyTrip), Sanjay Mohan (MakeMyTrip)
At MakeMyTrip, India’s leading online travel platform, customers were using voice or email to contact agents for post sale support. In order to improve the efficiency of agents and improve customer experience, MakeMyTrip developed a chatbot Myra using some of the latest advances in deep learning. In this talk, we will discuss the high level architecture and the business impact created by Myra. Read more.

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