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Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry

  • Panos AlexopoulosEmail author
  • Ronald Denaux
  • Jose Manuel Gomez-Perez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9088)

Abstract

Named Entity Resolution (NER) is an information extraction task that involves detecting mentions of named entities within texts and mapping them to their corresponding entities in a given knowledge resource. Systems and frameworks for performing NER have been developed both by the academia and the industry with different features and capabilities. Nevertheless, what all approaches have in common is that their satisfactory performance in a given scenario does not constitute a trustworthy predictor of their performance in a different one, the reason being the scenario’s different characteristics (target entities, input texts, domain knowledge etc.). With that in mind, in this paper we describe a metric-based Diagnostic Framework that can be used to identify the causes behind the low performance of NER systems in industrial settings and take appropriate actions to increase it.

Keywords

News Article Knowledge Resource Input Text Word Sense Disambiguation Lexical Ambiguity 
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 2015

Authors and Affiliations

  • Panos Alexopoulos
    • 1
    Email author
  • Ronald Denaux
    • 1
  • Jose Manuel Gomez-Perez
    • 1
  1. 1.Expert System IberiaMadridSpain

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