Entity Recognition in Information Extraction

  • Novita Hanafiah
  • Christoph Quix
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8397)


Detecting and resolving entities is an important step in information retrieval applications. Humans are able to recognize entities by context, but information extraction systems (IES) need to apply sophisticated algorithms to recognize an entity. The development and implementation of an entity recognition algorithm is described in this paper. The implemented system is integrated with an IES that derives triples from unstructured text. By doing so, the triples are more valuable in query answering because they refer to identified entities. By extracting the information from Wikipedia encyclopedia, a dictionary of entities and their contexts is built. The entity recognition computes a score for context similarity which is based on cosine similarity with a tf-idf weighting scheme and the string similarity. The implemented system shows a good accuracy on Wikipedia articles, is domain independent, and recognizes entities of arbitrary types.


Noun Phrase Ranking Function Cosine Similarity Entity Recognition Actual Entity 
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

  • Novita Hanafiah
    • 1
    • 2
  • Christoph Quix
    • 3
    • 4
  1. 1.Thai-German Graduate School of EngineeringKing Mongkut’s University of Technology North BangkokBangkokThailand
  2. 2.School of Computer ScienceBina Nusantara UniversityJakartaIndonesia
  3. 3.Fraunhofer Institute for Applied Information Technology FITSt. AugustinGermany
  4. 4.Information Systems and DatabasesRWTH Aachen UniversityAachenGermany

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