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

## TL;DR

This paper presents a fast, ontology-based Named Entity Recognition method tailored for real-time applications, effectively handling inflection variations in German and achieving high accuracy within milliseconds.

## Contribution

It introduces a novel inflection-tolerant NER approach using ontology and language sources, optimized for near-instant processing in real-time systems.

## Key findings

- NER process on German Wikipedia took less than an hour
- Precision and recall surpass comparable fast methods
- Quality gap with sophisticated NLP pipelines is reduced

## 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 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.