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Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications

Authors Christian Jilek, Markus Schröder, Rudolf Novik, Sven Schwarz, Heiko Maus, Andreas Dengel

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Author Details

Christian Jilek
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
  • Department of Computer Science, TU Kaiserslautern, Germany
Markus Schröder
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
  • Department of Computer Science, TU Kaiserslautern, Germany
Rudolf Novik
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
Sven Schwarz
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
Heiko Maus
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
Andreas Dengel
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
  • Department of Computer Science, TU Kaiserslautern, Germany

Funding

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - DE 420/19-1.

Acknowledgements

We thank Sven Hertling, Jörn Hees, Erfan Shamabadi, Oleksii Kotvytskyi and Tim Sprengart for their contributions in this project’s early and late phase, respectively.

Cite As Get BibTex

Christian Jilek, Markus Schröder, Rudolf Novik, Sven Schwarz, Heiko Maus, and Andreas Dengel. Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 11:1-11:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.LDK.2019.11

Abstract

A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems - just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines.
In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step, we address word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing real-time capable runtime performance.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Information extraction
  • Computing methodologies → Semantic networks
Keywords
  • Ontology-based information extraction
  • Named entity recognition
  • Inflectional languages
  • Real-time systems

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