Supporting Named Entity Recognition and Document Classification in a Knowledge Management System for Applied Gaming

Philippe Tamla, Florian Freund, Matthias Hemmje

Abstract

In this research paper, we present a system for named entity recognition and automatic document classification in an innovative knowledge management system for Applied Gaming. The objective of this project is to facilitate the management of machine learning-based named entity recognition models, that can be used for both: extracting different types of named entities and classifying textual documents from heterogeneous knowledge sources on the Web. We present real-world use case scenarios and derive features for training and managing NER models with the Stanford NLP machine learning API. Then, the integration of our developed NER system with an expert rule-based system is presented, which allows an automatic classification of textual documents into different taxonomy categories available in the knowledge management system. Finally, we present the results of a qualitative evaluation that was conducted to optimize the system user interface and enable a suitable integration into the target system.

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