TY  - CHAP
A1  - Klöser, Lars
A1  - Kohl, Philipp
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
T2  - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA
N2  - Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
Y1  - 2021
SN  - 978-989-758-526-5
U6  - http://dx.doi.org/10.5220/0010559201480156
N1  - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications, DeLTA2021, July 7-9, 2021
SP  - 148
EP  - 156
ER  - 
TY  - CHAP
A1  - Kohl, Philipp
A1  - Schmidts, Oliver
A1  - Klöser, Lars
A1  - Werth, Henri
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development
T2  - Quality of Information and Communications Technology. QUATIC 2021
N2  - The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.
KW  - Machine learning
KW  - Process model
KW  - Natural language processing
Y1  - 2021
SN  - 978-3-030-85346-4
SN  - 978-3-030-85347-1
U6  - http://dx.doi.org/10.1007/978-3-030-85347-1_12
N1  - International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal
SP  - 156
EP  - 166
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Schmidts, Oliver
A1  - Kraft, Bodo
A1  - Winkens, Marvin
A1  - Zündorf, Albert
T1  - Catalog integration of heterogeneous and volatile product data
T2  - DATA 2020: Data Management Technologies and Applications
N2  - The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
Y1  - 2021
SN  - 978-3-030-83013-7
U6  - http://dx.doi.org/10.1007/978-3-030-83014-4_7
N1  - International Conference on Data Management Technologies and Applications, DATA 2020, 7-9 July
SP  - 134
EP  - 153
PB  - Springer
CY  - Cham
ER  -