What we're about

This group is for everyone working in Natural Language Processing technologies and applications. Our online meetings (for now) and Philadelphia meetings (eventually) will provide opportunities to hear about and present innovative work and research, to learn about emerging technologies, network, and exchange ideas and brainstorm. 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.

Subscribe to the NLPxing YouTube channel at https://bit.ly/NLPxing for video of past meetup programs and follow us on Twitter at @PHLNLP.

Upcoming events (2)

NLP Summit 2022 (free partner event)

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A free partner event from group sponsor John Snow Labs --

NLP Summit 2022

We're excited to announce the program for the virtual NLP Summit 2022 on October 4-6! Join the the world’s largest applied NLP community for three days of immersive, industry-focused content including over 50 technical sessions. Admission is free.

Confirmed Keynotes Include

  • Sparse Expert Models: Past and Future
    by Barret Zoph, Research Scientist & Liam Fedus, Senior Research Scientist at Google Brain
  • Putting Zero-Shot, No-Code, and Responsible NLP to Work
    by David Talby, CTO at John Snow Labs
  • Deep Learning for Relation Extraction from Clinical Documents
    by Vishakha Sharma, Principal Scientist at Roche
  • Deploying BLOOM: A 176B Parameter Multi-Lingual Large Language Model
    by Sanchit Gandhi, Machine Learning Research Engineer & Suraj Patil, Machine Learning Engineer at Hugging Face
  • BERTopic: Topic Modeling by Combining the Old with the New
    by Maarten Grootendorst, Data Scientist at Integraal Kankercentrum Nederland
  • Evolution through Large Language Models
    by Joel Lehman, ML Research Scientist at Open AI

Register now to join live Q&A sessions with the speakers, connect with others through networking features, and access all content on-demand after the event.

The second week will feature beginner to advanced live training workshops with certifications. Learn, share, and apply best practices for putting AI to good use!

Data to Text and Sports Natural Language Generation (NLG)

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Our November 9 program features an online presentation on Data to Text and Sports Natural Language Generation (NLG) by Ehud Reiter and Jorge Costa of Arria NLG.

They will describe data-to-text NLG including technologies with a focus on sports story-writing. Our speakers will discuss sports NLG goals, challenges, and approaches and describe system function at a high level with knowledge-based technologies. There are complications that include difficulties using neural language models in sports NLG because of accuracy and hallucination problems. Ehud and Jorge will talk about challenges getting high-quality data and how computer vision can be used to automatically extract useful game data (for the NLG system) from video data.

Ehud Reiter is a Professor of Computing Science at the University of Aberdeen and the Chief Scientist of Arria NLG. He has been working in natural language generation for 35 years and is one of the most cited and respected researchers in this field. Prof Reiter has a Google Scholar h-index of 54 and is currently chair of the ACL Special Interest Group on Generation (SIGGEN). For more information about Prof Reiter, see ehudreiter.com.

Jorge Costa is Senior Vice President of Sports Analytics at Arria Boost, the sports media division of Arria NLG. He has over 15 years of experience at the intersection of sports, analytics, and the communication of complex data. After starting his career at the NBA League Office, Jorge moved to the team side as Director of Research and Technology for the Detroit Pistons (NBA), where he led the development of decision-making analytics tools and processes for the team's front office. Today, Jorge and his team of data scientists build scalable storytelling solutions, serving multiple sports and content types.

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Past events (35)

Few-shot Learning with Retrieval Augmented Language Models

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