Actions

HumAssist: Difference between revisions

Computational Linguistics and Information Processing

(Created page with "DARPA's Low Resource Languages for Emergent Incidents  (LORELEI)] program [https://www.darpa.mil/program/low-resource-languages-for-emergent-incidents] is focused on enablin...")
 
No edit summary
 
(3 intermediate revisions by one other user not shown)
Line 1: Line 1:
DARPA's Low Resource Languages for Emergent Incidents  (LORELEI)] program [https://www.darpa.mil/program/low-resource-languages-for-emergent-incidents]
 
is focused  on enabling  low-cost development of capabilities for low-resource languages, targeted at humanitarian assistance and disaster relief, HADR [https://health.mil/Military-Health-Topics/Health-Readiness/Global-Health-Engagement/Humanitarian-Assistance-and-Disaster-Relief HADR] in the aftermath of a crisis like an earthquake, tsunami, or epidemic.  In this project we are developing new technologies for quickly ramping up the ability to extract actionable information from online sources related to both population needs (e.g. food or water shortages, lack of shelter, need for medical assistance) and population mental state (e.g. fear, anger).  Methodologically we are focused on advanced topic models and combinations of topic models with deep learning methods.
'''Identifying Humanitarian Assistance Needs in Low Resource Languages
'''
 
DARPA's Low Resource Languages for Emergent Incidents  (LORELEI) program [https://www.darpa.mil/program/low-resource-languages-for-emergent-incidents]
is focused  on enabling  low-cost development of capabilities for low-resource languages, targeted at humanitarian assistance and disaster relief ([https://health.mil/Military-Health-Topics/Health-Readiness/Global-Health-Engagement/Humanitarian-Assistance-and-Disaster-Relief HADR]) in the aftermath of a crisis like an earthquake, tsunami, or epidemic.  In this project we are developing new technologies for quickly ramping up the ability to extract actionable information from online sources related to both population needs (e.g. food or water shortages, lack of shelter, need for medical assistance) and population mental state (e.g. fear, anger).  Methodologically we are focused on advanced topic models and combinations of topic models with deep learning methods.

Latest revision as of 15:58, 10 December 2017

Identifying Humanitarian Assistance Needs in Low Resource Languages

DARPA's Low Resource Languages for Emergent Incidents  (LORELEI) program [1] is focused on enabling low-cost development of capabilities for low-resource languages, targeted at humanitarian assistance and disaster relief (HADR) in the aftermath of a crisis like an earthquake, tsunami, or epidemic.  In this project we are developing new technologies for quickly ramping up the ability to extract actionable information from online sources related to both population needs (e.g. food or water shortages, lack of shelter, need for medical assistance) and population mental state (e.g. fear, anger).  Methodologically we are focused on advanced topic models and combinations of topic models with deep learning methods.