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PublicationControlled Randomness Improves the Performance of Transformer Models( 2024-03-19)
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PublicationUncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models( 2023-12)
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PublicationImproving Zero-Shot Text Matching for Financial Auditing with Large Language Models( 2023-08-22)
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PublicationContradiction Detection in Financial Reports( 2023-01-23)
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PublicationKPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents( 2022-12)
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PublicationUtilizing Representation Learning for Robust Text Classification Under Datasetshift( 2021)Within One-vs-Rest (OVR) classification, a classifier differentiates a single class of interest (COI) from the rest, i.e. any other class. By extending the scope of the rest class to corruptions (dataset shift), aspects of outlier detection gain relevancy. In this work, we show that adversarially trained autoencoders (ATA) representative of autoencoder-based outlier detection methods, yield tremendous robustness improvements over traditional neural network methods such as multi-layer perceptrons (MLP) and common ensemble methods, while maintaining a competitive classification performance. In contrast, our results also reveal that deep learning methods solely optimized for classification, tend to fail completely when exposed to dataset shift.
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PublicationCombining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions( 2020)In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems. In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems.
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PublicationTriple Classification Using Regions and Fine-Grained Entity Typing( 2019)A Triple in knowledge-graph takes a form that consists of head, relation, tail. Triple Classification is used to determine the truth value of an unknown Triple. This is a hard task for 1-to-N relations using the vector-based embedding approach. We propose a new region-based embedding approach using fine-grained type chains. A novel geometric process is presented to extend the vectors of pre-trained entities into n-balls (n-dimensional balls) under the condition that head balls shall contain their tail balls. Our algorithm achieves zero energy loss, therefore, serves as a case study of perfectly imposing tree structures into vector space. An unknown Triple (h, r, x) will be predicted as true, when x's n-ball is located in the r-subspace of h's n-ball, following the same construction of know n tails of h. The experiments are based on large datasets derived from the benchmark datasets WN11, FB13, and WN18. Our results show that the performance of the new method is related to the length of the type chain and the quality of pre-trained entity-embeddings, and that performances of long chains with well-trained entity-embeddings outperform other methods in the literature.
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PublicationTwo Attempts to Predict Author Gender in Cross-Genre Settings in Dutch( 2019)This paper describes the systems designed by the FraunhoferIAIS team at the CLIN29 shared task on cross-genre gender detection in Dutch. We show two alternative classification approaches: a rather standard one consisting of feature engineering and a random forest classifier; and an alternative one involving a LSTM classifier. Both are enhanced by a LDA model trained on stems. We considered various features such as frequency of function words, parts-of-speech and sentiment among others. We achieved 53.77% average accuracy in the cross-genre settings.
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PublicationTowards Shortest Paths via Adiabatic Quantum Computing( 2018)Since first working quantum computers are now available, accelerated developments of this technology may be expected. This will likely impact graph- or network analysis because quantum computers promise fast solutions for many problems in these areas. In this paper, we explore the use of adiabatic quantum computing in finding shortest paths. We devise an Ising energy minimization formulation for this task and discuss how to set up a system of quantum bits to find minimum energy states of the model. In simulation experiments, we numerically solve the corresponding Schrödinger equations and observe our approach to work well. This evidences that shortest path computation can at least be assisted by quantum computers.
