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

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

In: Maria Eskevich, Gerard de Melo, Christian Fäth, John P. McCrae, Paul Buitelaar, Christian Chiarcos, Bettina Klimek, Milan Dojchinovski (Hrsg.). Proceedings of the 2nd Conference on Language, Data and Knowledge. Conference on Language, Data and Knowledge (LDK-2019) May 20-23 Leipzig Germany Seiten 11-1 OpenAccess Series in Informatics (OASIcs) 70 Schloss Dagstuhl - Leibniz-Zentrum für Informatik Dagstuhl, Germany 2019.


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.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence