Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Proceedings of the 20th ACM international conference on Information and knowledge management
"I'm eating a sandwich in Glasgow": modeling locations with tweets
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Named entity recognition in tweets: an experimental study
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Location inference using microblog messages
Proceedings of the 21st international conference companion on World Wide Web
TwiNER: named entity recognition in targeted twitter stream
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
GeoTxt: a web API to leverage place references in text
Proceedings of the 7th Workshop on Geographic Information Retrieval
Classifying microblogs for disasters
Proceedings of the 18th Australasian Document Computing Symposium
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Location information is critical to understanding the impact of a disaster, including where the damage is, where people need assistance and where help is available. We investigate the feasibility of applying Named Entity Recognizers to extract locations from microblogs, at the level of both geo-location and point-of-interest. Our experimental results show that such tools once retrained on microblog data have great potential to detect the where information, even at the granularity of point-of-interest.