  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 are 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 too much runtime performance.
