| 1 |
Differentiating Geographic Movement Described in Text Documents ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Exploring Descriptions of Movement Through Geovisual Analytics
|
|
|
|
In: KN J Cartogr Geogr Inf (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Are BERTs Sensitive to Native Interference in L2 Production? ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Extractive Research Slide Generation Using Windowed Labeling Ranking ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Multi-document abstractive summarization using ILP based multi-sentence compression ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Identifying Emotional and Informational Support in Online Health Communities
|
|
|
|
In: 25th International Conference on Computational Linguistics, August 23-29, 2014. Dublin, Ireland. (2014)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Predicting Subjectivity Orientation of Online Forum Threads
|
|
|
|
In: Computational Linguistics and Intelligent Text Processing, 2013, Berlin: Springer-Verlag, pp. 109-120 (2013)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
GeoCAM: A geovisual analytics workspace to contextualize and interpret statements about movement
|
|
|
|
In: Journal of Spatial Information Science (2012)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads
|
|
|
|
In: Proceedings of the Twenty-Fourth International Conference on Computational Linguistics (COLING), 2012, Mumbai, India (2012)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Block-Suffix Shifting: Fast, Simultaneous Medical Concept Set Identification in Large Medical Record Corpora
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages
|
|
|
|
Abstract:
Comunicació presentada a: LREC 2016, Tenth International Conference on Language Resources and Evaluation, celebrada del 23 al 28 de maig de 2016 a Portorož, Eslovènia. ; Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.
|
|
Keyword:
Disaster response; Natural language processing; Supervised classification; Twitter
|
|
URL: http://hdl.handle.net/10230/36883
|
|
BASE
|
|
Hide details
|
|
|
|
|
