License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2019.11
URN: urn:nbn:de:0030-drops-103759
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10375/
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Jilek, Christian ; Schröder, Markus ; Novik, Rudolf ; Schwarz, Sven ; Maus, Heiko ; Dengel, Andreas

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

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OASIcs-LDK-2019-11.pdf (1 MB)

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.

BibTeX - Entry

@InProceedings{jilek_et_al:OASIcs:2019:10375,
  author =	{Christian Jilek and Markus Schr{\"o}der and Rudolf Novik and Sven Schwarz and Heiko Maus and Andreas Dengel},
  title =	{{Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications}},
  booktitle =	{2nd Conference on Language, Data and Knowledge (LDK 2019)},
  pages =	{11:1--11:14},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Maria Eskevich and Gerard de Melo and Christian F{\"a}th and John P. McCrae and Paul Buitelaar and Christian Chiarcos and Bettina Klimek and Milan Dojchinovski},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10375},
  URN =		{urn:nbn:de:0030-drops-103759},
  doi =		{10.4230/OASIcs.LDK.2019.11},
  annote =	{Keywords: Ontology-based information extraction, Named entity recognition, Inflectional languages, Real-time systems}
}

Keywords: Ontology-based information extraction, Named entity recognition, Inflectional languages, Real-time systems
Collection: 2nd Conference on Language, Data and Knowledge (LDK 2019)
Issue Date: 2019
Date of publication: 16.05.2019

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