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...") |
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DARPA's Low Resource Languages for Emergent Incidents (LORELEI)] program [https://www.darpa.mil/program/low-resource-languages-for-emergent-incidents] | 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 | 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. | ||
Revision as of 05:19, 8 December 2017
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.