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Cross-Lingual Classification of Crisis Data

  • Prashant Khare
  • Grégoire Burel
  • Diana Maynard
  • Harith Alani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)

Abstract

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model.

Keywords

Semantics Cross-lingual Multilingual Crisis informatics Tweet classification 

Notes

Acknowledgment

This work has received support from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 687847 (COMRADES).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK
  2. 2.Department of Computer ScienceUniversity of SheffieldSheffieldUK

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