informatik-vortraege Juni 2020

informatik-vortraege@lists.rwth-aachen.de

2 Teilnehmer
3 Diskussionen

Einladung Informatik-Oberseminar Malte Nuhn

von Sekretariat I6

+********************************************************************** * * * 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

2 Jahre, 1 Monat

Einladung: Informatik-Oberseminar Frau Rihan Hai

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 13. Juli 2020, 16:00 Uhr Zoom: <https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09> https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09 Meeting-ID: 956 7645 5814 Passwort: 302988 Referentin: M.Eng. Rihan Hai Lehrstuhl Informatik 5 Thema: Data integration and Metadata Management in Data Lakes Abstract: Although big data has been discussed for some years, it still has many research challenges, such as the variety of data. Non-integrated data management systems with heterogeneous schemas, query languages, and data models result in information silos. As traditional 'schema-on-write' approaches such as data warehouses cannot solve the challenges to efficiently integrate, access, and query the information silos, data lake systems have been proposed as a solution to this problem. Data lakes are repositories storing raw data in its original format and providing a common access interface. In this thesis, we present a comprehensive and flexible data lake architecture and the prototype system Constance. First, we propose a native mapping representation to capture the hierarchical structures of nested mappings and efficient mapping generation algorithms. Second, to provide a unified querying interface, we design a novel query rewriting engine that combines logical methods for data integration based on declarative mappings with the big data processing system Apache Spark. Third, we also study the formalism of the generated schema mappings as dependencies. Our algorithmic approach transforms schema mappings expressed in second-order logic to their logically equivalent first-order forms. Finally, we introduce clustering-based algorithms to discover relaxed functional dependencies, which enrich the metadata and improve data quality in the data lake. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

4 Jahre

Einladung: Informatik-Oberseminar Herr Markus Beutel

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 30. Juni 2020, 14.00-15.00 Uhr Zoom: <https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09> https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09 Meeting-ID: 956 7645 5814 Passwort: 302988 Referent: Dipl.-Kfm. Markus Beutel Thema: End-to-End-Integration von komplementären Mobilitätsdienstleistungen durch unternehmensübergreifende Anbieterkooperation Abstract: Reisenden steht heutzutage eine Vielzahl unterschiedlicher Verkehrsmodi zur Verfügung. Dabei können sich viele Fortbewegungsmittel aufgrund individueller Charakteristika gegenseitig ergänzen, anstatt sich zu substituieren. Durch eine zumindest in Teilen segmentierte und anbieterspezifische Bereitstellung von Verkehrsdienstleistungen können beispielsweise ökonomische und ökologische Ineffizienzen entstehen. Im Zentrum einer vollständigen Digitalisierung und Integration heterogener Mobilitätsdienstleistungen sollte daher der ganzheitliche Dienstleistungsprozess, über die gesamte Servicekette hinweg, in den Mittelpunkt gestellt werden. Das übergeordnete Ziel dieser Arbeit besteht in der Erforschung einer integrierten Bereitstellung sich ergänzender Mobilitätsdienstleistungen, über Unternehmensgrenzen hinweg. Ausgangspunkt dieser Arbeit bildet die Untersuchung einer spezifischen Mobilitätsplattform im Hinblick auf verschiedene Integrationsfaktoren. Auf Basis einer umfassenden Analyse von Mobilitätsplattformen und in Verbindung mit der Beschreibung eines organisatorischen Rollenmodells werden daraufhin mögliche Anbieterkooperationsszenarien beschrieben. Um die unternehmensübergreifende Integration auf Prozessebene zu betrachten, wird schließlich ein Ansatz zur Erweiterung eines Softwarewerkzeugs zur Fusion von Geschäftsprozessmodellen evaluiert. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

4 Jahre

2024

2023

2022

2021

2020

2019

2018

informatik-vortraege Juni 2020