Information Extraction for Social Media

Mena Badieh Habib, Maurice van Keulen

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Abstract

The rapid growth in IT in the last two decades has led to a growth in the amount of information available online. A new style for sharing information is social media. Social media is a continuously instantly updated source of information. In this position paper, we propose a framework for Information Extraction (IE) from unstructured user generated contents on social media. The framework proposes solutions to overcome the IE challenges in this domain such as the short context, the noisy sparse contents and the uncertain contents. To overcome the challenges facing IE from social media, State-Of-The-Art approaches need to be adapted to suit the nature of social media posts. The key components and aspects of our proposed framework are noisy text filtering, named entity extraction, named entity disambiguation, feedback loops, and uncertainty handling.
Original languageEnglish
Title of host publicationProceedings of the Third Workshop on Semantic Web and Information Extraction (SWAIE 2014)
Place of PublicationDublin
PublisherAssociation for Computational Linguistics (ACL)
Pages9-16
Number of pages8
ISBN (Print)978-1-873769-48-5
Publication statusPublished - Aug 2014
Event3rd Workshop on Semantic Web and Information Extraction, SWAIE 2014 - Dublin, Ireland
Duration: 24 Aug 201424 Aug 2014
Conference number: 3

Publication series

Name
PublisherAssociation for Computational Linguistics
VolumeW14-62

Workshop

Workshop3rd Workshop on Semantic Web and Information Extraction, SWAIE 2014
Abbreviated titleSWAIE
CountryIreland
CityDublin
Period24/08/1424/08/14

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Keywords

  • Relation/fact extraction
  • Named entity extraction
  • Information Extraction
  • Named entity disambiguation
  • Social media
  • Microblogs
  • Twitter
  • Named entity recognition
  • Named entity linking
  • Tweets

Cite this

Habib, M. B., & van Keulen, M. (2014). Information Extraction for Social Media. In Proceedings of the Third Workshop on Semantic Web and Information Extraction (SWAIE 2014) (pp. 9-16). Dublin: Association for Computational Linguistics (ACL).