Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models

  • Hendrik ter Horst
  • Matthias Hartung
  • Philipp Cimiano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)


The problems of recognizing mentions of entities in texts and linking them to unique knowledge base identifiers have received considerable attention in recent years. In this paper we present a probabilistic system based on undirected graphical models that jointly addresses both the entity recognition and the linking task. Our framework considers the span of mentions of entities as well as the corresponding knowledge base identifier as random variables and models the joint assignment using a factorized distribution. We show that our approach can be easily applied to different technical domains by merely exchanging the underlying ontology. On the task of recognizing and linking disease names, we show that our approach outperforms the state-of-the-art systems DNorm and TaggerOne, as well as two strong lexicon-based baselines. On the task of recognizing and linking chemical names, our system achieves comparable performance to the state-of-the-art.


Joint entity recognition and linking Undirected probabilistic graphical models Diseases Chemicals 



This work has been funded by the Federal Ministry of Education and Research (BMBF, Germany) in the PSINK project (project number 031L0028A).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hendrik ter Horst
    • 1
  • Matthias Hartung
    • 1
  • Philipp Cimiano
    • 1
  1. 1.Cognitive Interaction Technology Cluster of Excellence (CITEC), Semantic Computing GroupBielefeld UniversityBielefeldGermany

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