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Unsupervised Named Entity Recognition and Disambiguation: An Application to Old French Journals

  • Yusra Mosallam
  • Alaa Abi-Haidar
  • Jean-Gabriel Ganascia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8557)

Abstract

In this paper we introduce our method of Unsupervised Named Entity Recognition and Disambiguation (UNERD) that we test on a recently digitized unlabeled corpus of French journals comprising 260 issues from the 19th century. Our study focuses on detecting person, location, and organization names in text. Our original method uses a French entity knowledge base along with a statistical contextual disambiguation approach. We show that our method outperforms supervised approaches when trained on small amounts of annotated data, since manual data annotation is very expensive and time consuming, especially in foreign languages and specific domains.

Keywords

Noun Phrase Name Entity Recognition Word Sense Disambiguation Inverse Document Frequency Entity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yusra Mosallam
    • 1
  • Alaa Abi-Haidar
    • 2
  • Jean-Gabriel Ganascia
    • 2
  1. 1.DMKM Masters, UPMCParisFrance
  2. 2.ACASA, LIP6, UPMCParisFrance

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