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Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
(2021)
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.
Dieser "Crashkurs" eignet sich ausgezeichnet für die kompakte Wiederholung und die zielgerichtete Prüfungsvorbereitung. Das Buch ist aufgrund seiner fallbezogenen Ausrichtung vor allem für Anfänger gedacht, eignet sich aber auch für fortgeschrittene Studierende zur kompakten Wiederholung. Einfache Merksätze, Fälle, Übersichten, Definitionen und kurze Zusammenfassungen lassen sich leicht einprägen und geben Sicherheit für die Prüfung. Vorteile auf einen Blick: Das wichtigste BGB-Know-how als Repetitorium vor der Prüfung, mit erprobten Merksätzen und kurzen Zusammenfassungen,
Fall für Fall sicher durch die Prüfung
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.
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.
This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.
Im Anschluss an die arbeitsrechtlichen Jahresübersichten der vergangenen Jahre (NWB 5/2019 S. 266; NWB 8/2020 S. 557) gibt der nachfolgende Beitrag einen Überblick über relevante Entwicklungen im Arbeitsrecht des Jahres 2020. Der erste Teil beschäftigt sich hierbei mit gesetzlichen Änderungen. Neben Regelungen, die auf die COVID-19-Pandemie zurückgeführt werden können, hat der Gesetzgeber auch andere Vorhaben umgesetzt. So wurde u. a. das Arbeitnehmerentsenderecht reformiert. Für Diskussionen sorgt(e) zudem das Gesetzesvorhaben zur Regelung mobiler Arbeit. Im zweiten Teil werden für die Praxis wichtige höchstrichterliche Gerichtsentscheidungen zum Arbeitsrecht erläutert, alphabetisch sortiert von A (wie Antidiskriminierungsrecht) bis U (wie Urlaub).
Die Arbeitswelt ist im Umbruch. Der Bedarf an flexiblen Arbeitszeitmodellen nimmt stetig zu, wobei die Corona-Pandemie dieses Bedürfnis nochmals verschärft hat. Gerade auch in kleineren und mittleren Unternehmen wächst die Notwendigkeit, den Einsatz der Beschäftigten möglichst bedarfsgerecht zu steuern, also bei guter Auftragslage mehr Arbeitszeit abzurufen und bei ausbleibenden Aufträgen die Arbeitszeit zu reduzieren und somit bezahlte „Leerlaufzeiten“ zu vermeiden. Der Gesetzgeber stellt den Arbeitgebern hierfür das Instrument der sog. Abrufarbeit ( § 12 Teilzeit- und Befristungsgesetz – TzBfG ) zur Verfügung. In dem nachfolgenden Beitrag werden Möglichkeiten und Grenzen der Abrufarbeit skizziert und konkrete arbeitsvertragliche Gestaltungsmöglichkeiten aufgezeigt.
The treatment method to deactivate viable microorganisms from objects or products is termed sterilization. There are multiple forms of sterilization, each intended to be applied for a specific target, which depends on—but not limited to—the thermal, physical, and chemical stability of that target. Herein, an overview on the currently used sterilization processes in the global market is provided. Different sterilization techniques are grouped under a category that describes the method of treatment: radiation (gamma, electron beam, X-ray, and ultraviolet), thermal (dry and moist heat), and chemical (ethylene oxide, ozone, chlorine dioxide, and hydrogen peroxide). For each sterilization process, the typical process parameters as defined by regulations and the mode of antimicrobial activity are summarized. Finally, the recommended microorganisms that are used as biological indicators to validate sterilization processes in accordance with the rules that are established by various regulatory agencies are summarized.