Named Entity Recognition for Arabic Social Media

The majority of research on Arabic Named Entity Recognition (NER) addresses the the task for newswire genre, where the language used is Modern Standard Arabic (MSA), however, the need to study this task in social media is becoming more vital. Social media is characterized by the use of both MSA and Dialectal Arabic (DA), with often code switching between the two language varieties. Despite some common characteristics between MSA and DA, there are significant differences between which result in poor performance when MSA targeting systems are applied for NER in DA. Additionally, most NER systems rely primarily on gazetteers, which can be more challenging in a social media processing context due to an inherent low coverage. In this paper, we present a gazetteers-free NER system for Dialectal data that yields an F1 score of 72.68% which is an absolute improvement of 2 3% over a comparable state-ofthe-art gazetteer based DA-NER system.

• Publication year

  2015

• Venue

  VS@HLT-NAACL

• Publication date

  2015-06-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/v1/W15-1524
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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