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 - http://dx.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 - TY - CHAP A1 - Siebigteroth, Ines A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - A Study on Improving Corpus Creation by Pair Annotation T2 - Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK-PS 2019) Y1 - 2019 SN - 1613-0073 SP - 40 EP - 44 ER - 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. DeLTA 2023. Communications in Computer and Information Science 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 - http://dx.doi.org/978-3-031-39059-3 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 235 EP - 253 PB - Springer CY - Cham ER - TY - CHAP A1 - Sildatke, Michael A1 - Karwanni, Hendrik A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - Automated Software Quality Monitoring in Research Collaboration Projects T2 - ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Y1 - 2020 U6 - http://dx.doi.org/10.1145/3387940.3391478 SP - 603 EP - 610 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 - TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Winkens, Marvin A1 - Zündorf, Albert T1 - Catalog integration of low-quality product data by attribute label ranking T2 - Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA Y1 - 2020 SN - 978-989-758-440-4 U6 - http://dx.doi.org/10.5220/0009831000900101 SP - 90 EP - 101 ER - TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Schreiber, Marc A1 - Zündorf, Albert T1 - Continuously evaluated research projects in collaborative decoupled environments T2 - 2018 ACM/IEEE 5th International Workshop on Software Engineering Research and Industrial PracticePractice, May 29, 2018, Gothenburg, Sweden : SER&IP' 18 N2 - Often, research results from collaboration projects are not transferred into productive environments even though approaches are proven to work in demonstration prototypes. These demonstration prototypes are usually too fragile and error-prone to be transferred easily into productive environments. A lot of additional work is required. Inspired by the idea of an incremental delivery process, we introduce an architecture pattern, which combines the approach of Metrics Driven Research Collaboration with microservices for the ease of integration. It enables keeping track of project goals over the course of the collaboration while every party may focus on their expert skills: researchers may focus on complex algorithms, practitioners may focus on their business goals. Through the simplified integration (intermediate) research results can be introduced into a productive environment which enables getting an early user feedback and allows for the early evaluation of different approaches. The practitioners’ business model benefits throughout the full project duration. Y1 - 2018 SP - 1 EP - 9 PB - ACM CY - New York, NY ER - TY - CHAP A1 - Schreiber, Marc A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - Cost-efficient quality assurance of natural language processing tools through continuous monitoring with continuous integration T2 - 3rd International Workshop on Software Engineering Research and Industrial Practice Y1 - 2016 U6 - http://dx.doi.org/10.1145/2897022.2897029 N1 - SER&IP’16, May 17 2016, Austin, TX, USA SP - 46 EP - 52 ER - TY - CHAP A1 - Klöser, Lars A1 - Büsgen, André A1 - Kohl, Philipp A1 - Kraft, Bodo A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - Explaining relation classification models with semantic extents T2 - DeLTA 2023: Deep Learning Theory and Applications N2 - In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models. KW - Relation classification KW - Natural language processing KW - Natural language understanding KW - Information extraction KW - Trustworthy artificial intelligence Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - http://dx.doi.org/10.1007/978-3-031-39059-3_13 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 189 EP - 208 PB - Springer CY - Cham ER - TY - CHAP A1 - Büsgen, André A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - Exploratory analysis of chat-based black market profiles with natural language processing T2 - Proceedings of the 11th International Conference on Data Science, Technology and Applications N2 - Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods. KW - Clustering KW - Natural Language Processing KW - Information Extraction KW - Profile Extraction KW - Text Mining Y1 - 2022 SN - 978-989-758-583-8 U6 - http://dx.doi.org/10.5220/0011271400003269 SN - 2184-285X SP - 83 EP - 94 ER -