What we're about

This group is for everyone working in Natural Language Processing technologies and applications. Our New York City meetings will provide opportunities to hear about and present innovative work and research, to learn about emerging technologies, network, and exchange ideas and brainstorm, but we'll also meet online so this meetup is for everyone! Topics will include machine learning, computational linguistics, text analytics, speech processing, conversational systems, sentiment and emotion AI, and search and applications in finance, customer experience, online and social media, health sciences, and more.

If you're actively involved in NLP or just want to learn more, please join us, and follow us on Twitter at @NYNLPmeetup (https://twitter.com/NYNLPmeetup).

Upcoming events (2)

Deep Learning for NLP with Distributed Training + Hybrid NLP on Edge Computers

Our October 1 program will have two presentations, 1) Faster Deep Learning for NLP with Distributed Training followed by 2) Hybrid NLP on Edge Computers. The program starts at 2 pm US-Eastern / 7 pm UK/Ireland / 8 pm CEST / 11 am US-Pacific. Talk #1, Faster Deep Learning for NLP with Distributed Training, features Dave Troiano, a Solutions Engineer at Determined AI. His abstract: In this talk, we reduce BERT training time by more than an order of magnitude with the Determined Deep Learning Training Platform's distributed training capability. Distributed training is a technique in which we leverage multiple GPUs, potentially spread across multiple machines, in order to speed up the overall time it takes to train a model. Moving from a single GPU to multiple is rife with challenges spanning machine learning and systems, and we'll show how Determined AI's open source platform helps NLP practitioners surmount these challenges in order to experiment more quickly and easily than ever before. Dave Troiano is a solutions engineer at Determined AI, the open source deep learning training platform. Previously, he has worked in text analytics as an engineer at Basis Technology, and in the text-based search & discovery space as an SE at Lucidworks. Talk #2, Hybrid NLP on Edge Computers, features Antonio Linari, chief technology officer at Expert System Enterprise. His abstract: Find out how by mixing symbolic AI with traditional deep learning language modeling, it's possible to reach NLP SOTA on edge computers like Nvidia Jetson Nano. Starting from Wikipedia and running the full NLP pipeline, based on semantic network and word sense disambiguation, is possible to train unified language model, fine tuning - ULMFiT to work with real contextual word embeddings. As an example the IMDB corpus will be used to train a model for sentiment analysis. By responding here, you acknowledge and consent to our Code of Conduct: We seek to provide a respectful, friendly, professional experience for everyone regardless of gender, sexual orientation, physical appearance, disability, age, race, and religion. We do not tolerate behavior that is harassing or degrading to any individual, in any form. Participants are responsible for knowing and abiding by these standards. We encourage all attendees to assist in creating a welcoming, safe, and respectful experience. We are grateful for meetup support provided by Spark NLP publisher John Snow Labs (https://www.johnsnowlabs.com/).

DeText: A Framework for Deep Natural Language Understanding at LinkedIn

MEETUP POSTPONED -- A DATE WILL BE SET NEXT WEEK (SEPT 14 OR AFTER) Our next meetup features a presentation on "DeText: A Framework for Deep Natural Language Understanding at LinkedIn." The program starts at 12 noon US-Eastern / 9 am US-Pacific / 5 pm BST / 6 pm CEST. The speakers are: Xiaowei Liu, a senior software engineer in the NLP team at LinkedIn where she focuses on applying state-of-the-art NLP algorithms to power and improve LinkedIn products. She received her Ph.D. in EE from Stony Brook University, and B.S. from Beijing University of Posts and Telecommunications. Sida Wang, a senior software engineer in the NLP team at LinkedIn. He obtained his M.S. from Carnegie Mellon University and his B.S. from Peking University. His work focuses on ranking and language modeling. Dr. Weiwei Guo, who leads the NLP team at LinkedIn. He has broad interests in NLP and Information Retrieval. He obtained his Ph.D. with a focus on NLP from Columbia University, and B.S. from Sun Yat-sen University. Weiwei has published over 20 peer-reviewed papers in top conferences including ACL, EMNLP, NAACL, SIGIR, KDD with thousands of citations. Dr. Huiji Gao, who leads the AI Algorithms Foundation team at LinkedIn. He has broad interests in machine learning/AI and its applications, including search/recommender systems, computational advertising, and NLP. He received Ph.D. in Computer Science from Arizona State University, and B.S./M.S. from Beijing University of Posts and Telecommunications. He has filed over 10 U.S. patents and published 40 publications in top journals and conferences including KDD, AAAI, WWW, ICDM, DMKD with thousands of citations." Here's an article introducing DeText and describing how DeText grants new capabilities to popular NLP models and illustrating how neural ranking is designed and developed in DeText: https://engineering.linkedin.com/blog/2020/open-sourcing-detext By responding here, you acknowledge and consent to our Code of Conduct: We seek to provide a respectful, friendly, professional experience for everyone regardless of gender, sexual orientation, physical appearance, disability, age, race, and religion. We do not tolerate behavior that is harassing or degrading to any individual, in any form. Participants are responsible for knowing and abiding by these standards. We encourage all attendees to assist in creating a welcoming, safe, and respectful experience. We are grateful for meetup support provided by Spark NLP publisher John Snow Labs (https://www.johnsnowlabs.com/).

Past events (22)

Extracting Personal Events from Dialogue

Online event

Photos (73)

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