Abstract
This talk presents two NLP systems that were developed for helping disaster victims and rescue workers in the aftermath of large-scale disasters. DISAANA provides answers to questions such as “What is in short supply in Tokyo?” and displays locations related to each answer on a map. D-SUMM automatically summarizes a large number of disaster related reports concerning a specified area and helps rescue workers to understand disaster situations from a macro perspective. Both systems are publicly available as Web services. In the aftermath of the 2016 Kumamoto Earthquake (M7.0), the Japanese government actually used DISAANA to analyze the situation.- Anthology ID:
- W16-3903
- Volume:
- Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Bo Han, Alan Ritter, Leon Derczynski, Wei Xu, Tim Baldwin
- Venue:
- WNUT
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 3
- Language:
- URL:
- https://aclanthology.org/W16-3903
- DOI:
- Cite (ACL):
- Kentaro Torisawa. 2016. DISAANA and D-SUMM: Large-scale Real Time NLP Systems for Analyzing Disaster Related Reports in Tweets. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), page 3, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- DISAANA and D-SUMM: Large-scale Real Time NLP Systems for Analyzing Disaster Related Reports in Tweets (Torisawa, WNUT 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W16-3903.pdf