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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.
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.
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.
An acetoin biosensor based on a capacitive electrolyte–insulator–semiconductor (EIS) structure modified with the enzyme acetoin reductase, also known as butane-2,3-diol dehydrogenase (Bacillus clausii DSM 8716ᵀ), is applied for acetoin detection in beer, red wine, and fermentation broth samples for the first time. The EIS sensor consists of an Al/p-Si/SiO₂/Ta₂O₅ layer structure with immobilized acetoin reductase on top of the Ta₂O₅ transducer layer by means of crosslinking via glutaraldehyde. The unmodified and enzyme-modified sensors are electrochemically characterized by means of leakage current, capacitance–voltage, and constant capacitance methods, respectively.
Glucose oxidase (GOx) is an enzyme frequently used in glucose biosensors. As increased temperatures can enhance the performance of electrochemical sensors, we investigated the impact of temperature pulses on GOx that was drop-coated on flattened Pt microwires. The wires were heated by an alternating current. The sensitivity towards glucose and the temperature stability of GOx was investigated by amperometry. An up to 22-fold increase of sensitivity was observed. Spatially resolved enzyme activity changes were investigated via scanning electrochemical microscopy. The application of short (<100 ms) heat pulses was associated with less thermal inactivation of the immobilized GOx than long-term heating.
Stretch-shortening type actions are characterized by lengthening of the pre-activated muscle-tendon unit (MTU) in the eccentric phase immediately followed by muscle shortening. Under 1 g, pre-activity before and muscle activity after ground contact, scale muscle stiffness, which is crucial for the recoil properties of the MTU in the subsequent push-off. This study aimed to examine the neuro-mechanical coupling of the stretch-shortening cycle in response to gravity levels ranging from 0.1 to 2 g. During parabolic flights, 17 subjects performed drop jumps while electromyography (EMG) of the lower limb muscles was combined with ultrasound images of the gastrocnemius medialis, 2D kinematics and kinetics to depict changes in energy management and performance. Neuro-mechanical coupling in 1 g was characterized by high magnitudes of pre-activity and eccentric muscle activity allowing an isometric muscle behavior during ground contact. EMG during pre-activity and the concentric phase systematically increased from 0.1 to 1 g. Below 1 g the EMG in the eccentric phase was diminished, leading to muscle lengthening and reduced MTU stretches. Kinetic energy at take-off and performance were decreased compared to 1 g. Above 1 g, reduced EMG in the eccentric phase was accompanied by large MTU and muscle stretch, increased joint flexion amplitudes, energy loss and reduced performance. The energy outcome function established by linear mixed model reveals that the central nervous system regulates the extensor muscles phase- and load-specifically. In conclusion, neuro-mechanical coupling appears to be optimized in 1 g. Below 1 g, the energy outcome is compromised by reduced muscle stiffness. Above 1 g, loading progressively induces muscle lengthening, thus facilitating energy dissipation.