Abstract

During the course of a Humanitarian Assistance-Disaster Relief (HADR) crisis, that can happen anywhere in the world, real-time information is often posted online by the people in need of help which, in turn, can be used by different stakeholders involved with management of the crisis. Automated processing of such posts can considerably improve the effectiveness of such efforts by, for example, understanding the aggregated emotion from affected populations in specific areas, which may help inform decision-makers on how to best allocate resources for an effective disaster response. However, these efforts may be severely limited by the availability of resources for a particular local language. The ongoing DARPA project Low Resource Languages for Emergent Incidents (LORELEI) aims to further language processing technologies for low resource languages in the context of such a humanitarian crisis. In this work …

Date

2019

Authors

Victor R Martinez, Anil Ramakrishna, Ming-Chang Chiu, Karan Singla, Shrikanth Narayanan

Conference

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

Pages

1-6

Publisher

IEEE