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 text documents from different 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 text documents into different taxonomy categories available in the knowledge management system. Finally, we present the results of two evaluations. First, a functional evaluation that demonstrates the portability of our NER system using a standard text corpus in the medical area. Second, a qualitative evaluation that was conducted to optimize the overall user interface of our system and enable a suitable integration into the target environment.

This article focuses on assisting medical professionals in analyzing domain-specific texts and selecting and comparing Named Entity Recognition (NER) frameworks. It details the development and evaluation of a system that utilizes a generic approach alongside the structured Nunamaker methodology. This system empowers medical professionals to train, evaluate, and compare NER models across diverse frameworks, such as Stanford CoreNLP, spaCy, and Hugging Face Transformers, independent of their specific implementations. Additionally, it introduces a concept for modeling a general training and evaluation process. Finally, experiments using various ontologies from the CRAFT corpus are conducted to assess the effectiveness of the current prototype.

In this article, we present Stanford Named Entity Recognition and Classification (SNERC), an intelligent system designed to enhance knowledge management through named entity recognition (NER) and document classification (DC) in the field of Applied Gaming. In this domain, the effective application of NER and DC is essential for addressing information overload (IO), enabling software developers to efficiently search, filter, and retrieve large volumes of textual data from web sources. SNERC streamlines the management and deployment of machine learning (ML)-based NER models, supporting the accurate extraction of named entities (NEs) and the classification of heterogeneous textual documents. The system tackles key challenges in NER, such as the impact of language and domain specificity on model performance, domain adaptation, and the complexity of handling diverse NE types. We demonstrate SNERC’s capabilities through real-world use cases, highlighting improvements in DC and information retrieval (IR) within applied gaming scenarios. The system provides core functionalities for training, evaluating, and managing NER models using the Stanford CoreNLP framework. Additionally, SNERC integrates with a rule-based expert system (RBES) to enable the automatic categorization of documents into predefined taxonomies within a knowledge management system. We present results from comprehensive qualitative and quantitative evaluations—measured through precision, recall, and F-score—to assess the system’s effectiveness and identify areas for further optimization, supporting seamless integration into real-world operational environments. Received: 15 July 2024 | Revised: 9 April 2025 | Accepted: 29 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Philippe Tamla: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Florian Freund: Methodology, Software, Validation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Matthias Hemmje: Conceptualization, Validation, Supervision, Project administration.