Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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NLP Architect by Intel's AI Lab

12:0512:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Average rating: ****.
(4.67, 3 ratings)

Who is this presentation for?

  • NLP researchers and developers and business analysts



What you'll learn

  • Learn how to deploy NLP Architect for building NLP-based solutions


Deep learning, powerful computing resources, and greater access to useful datasets have driven many advances in natural language processing (NLP) in recent years. Intel AI Lab’s team of NLP researchers and developers recently released NLP Architect, an open source library fully based on DL topologies, as a platform for future research and collaborations.

Moshe Wasserblat offers an overview of NLP Architect and demonstrates how it makes it easy for non-ML/NLP developers to build advanced NLP solutions, for example, unsupervised sentiment extraction, set term expansion, and topic and trend extraction. NLP Architect comes with a large number of models for implementing SOTA algorithms and provides end-to-end examples of training and inference processes. In addition, the library is modularized for easy integration and includes a number of functionalities, such as data pipelines, dataset loader, common functional calls, and utilities related to NLP.

Photo of Moshe Wasserblat

Moshe Wasserblat


Moshe Wasserblat is the Natural Language Processing and Deep Learning Research Group manager for Intel’s Artificial Intelligence Products Group. Previously, he was with NICE Systems for more than 17 years, where he founded and led the speech and text analytics research team. His interests are in the field of speech processing and natural language processing. He was the cofounder and coordinator of the EXCITEMENT FP7 ICT program and served as organizer and manager of several initiatives, including many Israeli chief scientist programs. He has filed more than 60 patents in the field of language technology and has several publications in international conferences and journals. His areas of expertise include speech recognition, conversational natural language processing, emotion detection, speaker separation, speaker recognition, deep learning, and machine learning.