Sep 23–26, 2019

Schedule: Text and Language processing and analysis sessions

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 you need to decide what to do when you run into text data. Alice Zhao walks you through the steps to turn text data into a format that a machine can understand, explores some of the most popular text analytics techniques, and showcases several natural language processing (NLP) libraries in Python, including NLTK, TextBlob, spaCy, and gensim. Read more.
1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 23/24
David Talby (Pacific AI), Alex Thomas (John Snow Labs), Saif Addin Ellafi (John Snow Labs), Claudiu Branzan (Accenture)
David Talby, Alex Thomas, Saif Addin Ellafi, and Claudiu Branzan walk you through state-of-the-art natural language processing (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.
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 (RNNs) and variants (long short-term memory [LSTM] and gated recurrent unit [GRU]), and convolutional neural networks. Read more.
1:15pm1:55pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Saif Addin Ellafi (John Snow Labs), Scott Hoch (BlackBox Engineering)
Recruiting patients for clinical trials is a major challenge in drug development. Saif Addin Ellafi and Scott Hoch explain how Deep 6 uses Spark NLP to scale its training and inference pipelines to millions of patients while achieving state-of-the-art accuracy. They dive into the technical challenges, the architecture of the full solution, and the lessons the company learned. Read more.
2:05pm2:45pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Panos Alexopoulos (Textkernel)
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. Panos Alexopoulos presents real-world cases where making fine, seemingly pedantic, distinctions in the meaning of data science tasks and the related data has helped improve significantly the effectiveness and value. Read more.
2:55pm3:35pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Gerard de Melo (Rutgers University)
Gerard de Melo takes a deep dive into the kinds of sentiment and emotion consumers associate with a text. With new data-driven approaches, organizations can better pay attention to what's being said about them in different markets. And you can consider fonts and palettes best suited to convey specific emotions, so organizations can make informed choices when presenting information to consumers. Read more.
4:35pm5:15pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
John Berryman (Eventbrite)
Eventbrite is exploring a new machine learning approach that allows it to harvest data from customer search logs and automatically tag events based upon their content. John Berryman dives into the results and how they have allowed the company to provide users with a better inventory-browsing experience. Read more.
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. Vlad Eidelman outlines how natural language processing (NLP) and machine learning can be used to automate the process by analyzing over 10 million publicly released comments. Read more.
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? Sireesha Muppala, Shelbee Eigenbrode, and Emily Webber tackle the problem of men talking over or down to women and its impact on career progression for women. They also demonstrate an Alexa skill that uses deep learning techniques on incoming audio feeds, examine ownership of the problem for women and men, and suggest helpful strategies. Read more.
1:15pm1:55pm Thursday, September 26, 2019
Location: 1A 12/14
Sandra Carrico (GLYNT)
Sandra Carrico explores 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.
3:45pm4:25pm Thursday, September 26, 2019
Location: 1E 12/13
Madhu Gopinathan (MakeMyTrip), Sanjay Mohan (MakeMyTrip)
At MakeMyTrip customers were using voice or email to contact agents for postsale 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. Madhu Gopinathan and Sanjay Mohan explain the high-level architecture and the business impact Myra created. Read more.

    Contact us

    confreg@oreilly.com

    For conference registration information and customer service

    partners@oreilly.com

    For more information on community discounts and trade opportunities with O’Reilly conferences

    strataconf@oreilly.com

    For information on exhibiting or sponsoring a conference

    pr@oreilly.com

    For media/analyst press inquires