The following is a guest post from Charlie Greenbacker, organizer of the DC NLP meetup. Curious about techniques and methods for applying data science to unstructured text?
The DC NLP meetup group is for anyone in the Washington, D.C. area working in, or interested in learning about, Natural Language Processing (NLP). Our meetings will be an opportunity for folks to network, give presentations about their work or research projects, learn about the latest advancements in our field, and exchange ideas or brainstorm. Topics may include computational linguistics, machine learning, text analytics, data mining, information extraction, speech processing, sentiment analysis, and much more.
Building on the tremendous success of our inaugural meetup in April (cross-listed with our friends at Data Science DC), we're ready to strike out on our own and start holding regular monthly meetups. We've recently made arrangements with the great folks at Stetsons Famous Bar & Grill in Adams Morgan to host our meetups in their upstairs bar on the second Wednesday of each month. We're truly excited to see you all again and will kick things off with a great set of presentations.
At our next meetup on Wednesday, Sept 11, we'll host three 20-minute talks on various NLP topics:
• Many of our new members are just getting started in natural language processing and have indicated an interest in learning some of the basics.Charlie Greenbacker, Principal Data Scientist at Berico Technologies, will provide a "crash course" in NLP techniques and applications.
• Ben Johnson is a co-founder of OpenWIMs.org – a set of standards and implementations of knowledge-based and stochastic semantic text analyzers. Ben will briefly describe the OpenWIMs system and give a demo of the latest OpenWIMs build.
• Valerie Coffman is the founder of Feastie.com, a search engine for recipes from food blogs, and currently a developer and data scientist working with Synapsify. She will present the unique text analytics methods used by Synapsify to measure quality and themes in writing.