Welcome to the UPF Digital Repository

Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages

Show simple item record

dc.contributor.author Imran, Muhammad
dc.contributor.author Mitra, Prasenjit
dc.contributor.author Castillo, Carlos
dc.date.accessioned 2019-03-21T13:45:25Z
dc.date.available 2019-03-21T13:45:25Z
dc.date.issued 2016
dc.identifier.citation Imran M, Mitra P, Castillo C. Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages. In: Calzolari N, Choukri K, Declerck T, Goggi S, Grobelnik M, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S. LREC 2016, Tenth International Conference on Language Resources and Evaluation; 2016 23-28 May; Portorož, Slovenia. [Portorož]: LREC, 2016. p. 1638-43.
dc.identifier.isbn 978-2-9517408-9-1
dc.identifier.uri http://hdl.handle.net/10230/36883
dc.description 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.
dc.description.abstract
dc.description.abstract 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher LREC
dc.relation.ispartof Calzolari N, Choukri K, Declerck T, Goggi S, Grobelnik M, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S. LREC 2016, Tenth International Conference on Language Resources and Evaluation; 2016 23-28 May; Portorož, Slovenia. [Portorož]: LREC, 2016. p. 1638-43.
dc.rights © The European Language Resources Association. The LREC 2016 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
dc.rights.uri https://creativecommons.org/licenses/by-nc/4.0/
dc.title Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages
dc.type info:eu-repo/semantics/conferenceObject
dc.subject.keyword Natural language processing
dc.subject.keyword Twitter
dc.subject.keyword Disaster response
dc.subject.keyword Supervised classification
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking