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 - 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 - TY - CHAP A1 - Büsgen, André A1 - Klöser, Lars A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Zündorf, Albert ED - Cuzzocrea, Alfredo ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Quix, Christoph T1 - From cracked accounts to fake IDs: user profiling on German telegram black market channels T2 - Data Management Technologies and Applications N2 - Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling. KW - Clustering KW - Natural language processing KW - Information extraction KW - Profile extraction KW - Text mining Y1 - 2023 SN - 978-3-031-37889-8 (Print) SN - 978-3-031-37890-4 (Online) U6 - http://dx.doi.org/10.1007/978-3-031-37890-4_9 N1 - 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022 SP - 176 EP - 202 PB - Springer CY - Cham 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 -