2017
IJCNLP
IJCNLP 2017
CADET: Computer Assisted Discovery Extraction and Translation
Abstract
AbstractComputer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.
👥
Mega-Team
— 27 authors
🌉
Interdisciplinary Bridge
— Computer Science and Natural Language Processing
🧭
Keyword Pioneer
— microservice architecture
🐣
Hot Topic Early Bird
— document analysis
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Benjamin Van Durme
,
Tom Lippincott
,
Kevin Duh
,
Deana Burchfield
,
Adam Poliak
,
Cash Costello
,
Tim Finin
,
Scott Miller
,
James Mayfield
,
Philipp Koehn
,
Craig Harman
,
Dawn Lawrie
,
Chandler May
,
Max Thomas
,
Annabelle Carrell
,
Julianne Chaloux
,
Tongfei Chen
,
Alex Comerford
,
Mark Dredze
,
Benjamin Glass
,
Shudong Hao
,
Patrick Martin
,
Pushpendre Rastogi
,
Rashmi Sankepally
,
Travis Wolfe
,
Ying-Ying Tran
,
Ted Zhang