TY  - CHAP
A1  - Kohl, Philipp
A1  - Freyer, Nils
A1  - Krämer, Yoka
A1  - Werth, Henri
A1  - Wolf, Steffen
A1  - Kraft, Bodo
A1  - Meinecke, Matthias
A1  - Zündorf, Albert
ED  - Conte, Donatello
ED  - Fred, Ana
ED  - Gusikhin, Oleg
ED  - Sansone, Carlo
T1  - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP
T2  - Deep Learning Theory and Applications
N2  - Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.

However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.

The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
KW  - Active learning
KW  - Query learning
KW  - Natural language processing
KW  - Deep learning
KW  - Reproducible research
Y1  - 2023
SN  - 978-3-031-39058-6 (Print)
SN  - 978-3-031-39059-3 (Online)
U6  - https://doi.org/10.1007/978-3-031-39059-3_16
N1  - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023.
SP  - 235
EP  - 253
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Sildatke, Michael
A1  - Karwanni, Hendrik
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - A distributed microservice architecture pattern for the automated generation of information extraction pipelines
JF  - SN Computer Science
N2  - Companies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use.
KW  - Architectural design
KW  - Model-driven software engineering
KW  - Software and systems modeling
KW  - Enterprise information systems
KW  - Information extraction
Y1  - 2023
U6  - https://doi.org/10.1007/s42979-023-02256-4
SN  - 2661-8907
N1  - Corresponding authors: Michael Sildatke, Hendrik Karwanni
IS  - 4, Article number: 833
PB  - Springer Singapore
CY  - Singapore
ER  -