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August 2019

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Einladung Informatik-Oberseminar Malte Nuhn
by Sekretariat I6 30 May '22

30 May '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 12. Juli 2019, 10.00 Uhr Ort: Informatikzentrum, E3, Raum 9222 Referent: Dipl.-Inform. Malte Nuhn Thema: Unsupervised Training with Applications in Natural Language Processing// Abstract: The state-of-the-art algorithms for various natural language processing tasks require large amounts of labeled training data. At the same time, obtaining labeled data of high quality is often the most costly step in setting up natural language processing systems.Opposed to this, unlabeled data is much cheaper to obtain and available in larger amounts.Currently, only few training algorithms make use of unlabeled data. In practice, training with only unlabeled data is not performed at all. In this thesis, we study how unlabeled data can be used to train a variety of models used in natural language processing. In particular, we study models applicable to solving substitution ciphers, spelling correction, and machine translation. This thesis lays the groundwork for unsupervised training by presenting and analyzing the corresponding models and unsupervised training problems in a consistent manner.We show that the unsupervised training problem that occurs when breaking one-to-one substitution ciphers is equivalent to the quadratic assignment problem (QAP) if a bigram language model is incorporated and therefore NP-hard. Based on this analysis, we present an effective algorithm for unsupervised training for deterministic substitutions. In the case of English one-to-one substitution ciphers, we show that our novel algorithm achieves results close to human performance, as presented in [Shannon 49]. Also, with this algorithm, we present, to the best of our knowledge, the first automatic decipherment of the second part of the Beale ciphers.Further, for the task of spelling correction, we work out the details of the EM algorithm [Dempster & Laird + 77] and experimentally show that the error rates achieved using purely unsupervised training reach those of supervised training.For handling large vocabularies, we introduce a novel model initialization as well as multiple training procedures that significantly speed up training without hurting the performance of the resulting models significantly.By incorporating an alignment model, we further extend this model such that it can be applied to the task of machine translation. We show that the true lexical and alignment model parameters can be learned without any labeled data: We experimentally show that the corresponding likelihood function attains its maximum for the true model parameters if a sufficient amount of unlabeled data is available. Further, for the problem of spelling correction with symbol substitutions and local swaps, we also show experimentally that the performance achieved with purely unsupervised EM training reaches that of supervised training. Finally, using the methods developed in this thesis, we present results on an unsupervised training task for machine translation with a ten times larger vocabulary than that of tasks investigated in previous work. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________________________ -- -- Stephanie Jansen Faculty of Mathematics, Computer Science and Natural Sciences HLTPR - Human Language Technology and Pattern Recognition RWTH Aachen University Ahornstraße 55 D-52074 Aachen Tel. Frau Jansen: +49 241 80-216 06 Tel. Frau Andersen: +49 241 80-216 01 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de Tel: +49 241 80-216 01/06 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de

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Einladung Informatik Oberseminar Simon Hacks
by Hacks, Simon 13 Aug '19

13 Aug '19

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 12. September 2019, 10.00 Uhr Ort: AH VI (2356|051), Ahornstr. 55 Referent: Simon Hacks, M.Sc. Lehr- und Forschungsgebiet Informatik 3 Thema: Improving the Quality of Enterprise Architecture Models: Processes and Techniques Abstract: Information technology (IT) pervades organizations more and more and becomes increasingly important for their business models. It has evolved from a purely supportive role to an important strategic pillar in many organizations. Even more, it is important that IT is aligned to the needs of the organization. Approaches that realize this are often subsumed under the term "business-IT-alignment". One instrument for achieving business-IT-alignment is Enterprise Architecture (EA). EAs provide a holistic perspective on the structure of the organization and provide a set of techniques to guide and steer the evolution of the organization to a desired goal state. A key artifact of EA is the EA model. It abstracts the elements and their relationships to an understandable and manageable measure. Usually enterprise architects model business processes, applications, hardware components, data models and customer relationships. Based on the information stored in the EA model, the organization's management makes important decisions regarding future focus. Contrary, also on the operational level, the model can provide important information, for example which application is used in which business environment and exchanges data with other applications. In order to be able to derive meaningful decisions from the EA model, their quality is of crucial importance. Therefore, this work is elaborates on developing different processes and techniques that ensure the quality of the EA model. First, we present a process to ensure the quality of the EA model, where model maintenance is understood as a continuous evolution. For this purpose, we define different steps, which have to be considered in such a process. This process will serve as foundation for a continuous delivery pipeline that will help automate as many of these steps as possible. Next, we present an approach that allows storing contrary information in EA models. In addition to the aforementioned processes, we developed also several techniques to improve the quality of EA models. However, for improvement, we need a method to evaluate the quality, which we also introduce within this work. Subsequently, we facilitate machine-learning techniques to support the modeler reuse existing elements of the model. In addition, we compare the performance of different algorithms to determine the best on in a certain situation. Additionally, we present a method to identify unnecessary elements in the model. Es laden ein: die Dozentinnen und Dozenten der Informatik

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