@inproceedings{SchreiberHirtbachKraftetal.2013, author = {Schreiber, Marc and Hirtbach, Stefan and Kraft, Bodo and Steinmetzler, Andreas}, title = {Software in the city: visual guidance through large scale software projects}, series = {Software Engineering 2013 : Fachtagung des GI-Fachbereichs Softwaretechnik, 26. Februar-1. M{\"a}rz 2013 in Aachen. (GI-Edition ; 213)}, booktitle = {Software Engineering 2013 : Fachtagung des GI-Fachbereichs Softwaretechnik, 26. Februar-1. M{\"a}rz 2013 in Aachen. (GI-Edition ; 213)}, editor = {Kowalewski, Stefan}, publisher = {Ges. f{\"u}r Informatik}, address = {Bonn}, isbn = {978-3-88579-607-7 ; 978-3-88579-609-1}, pages = {213 -- 224}, year = {2013}, language = {en} } @inproceedings{SchreiberBarkschatKraft2014, author = {Schreiber, Marc and Barkschat, Kai and Kraft, Bodo}, title = {Using Continuous Integration to organize and monitor the annotation process of domain specific corpora}, series = {5th International Conference on Information and Communication Systems (ICICS) : 1-3 April 2014, Irbid, Jordanien}, booktitle = {5th International Conference on Information and Communication Systems (ICICS) : 1-3 April 2014, Irbid, Jordanien}, organization = {International Conference on Information and Communication Systems <5, 2014, Irbid, Jordanien>}, isbn = {978-1-4799-3022-7}, doi = {10.1109/IACS.2014.6841958}, pages = {1 -- 6}, year = {2014}, language = {en} } @article{SchreiberBarkschatKraftetal.2015, author = {Schreiber, Marc and Barkschat, Kai and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {Quick Pad Tagger : An Efficient Graphical User Interface for Building Annotated Corpora with Multiple Annotation Layers}, series = {Computer Science \& Information Technology (CS \& IT)}, volume = {5}, journal = {Computer Science \& Information Technology (CS \& IT)}, number = {4}, publisher = {Academy \& Industry Research Collaboration Center (AIRCC)}, isbn = {978-1-921987-32-8}, issn = {2231 - 5403}, doi = {10.5121/csit.2015.50413}, pages = {131 -- 143}, year = {2015}, language = {en} } @inproceedings{SchreiberKraftZuendorf2016, author = {Schreiber, Marc and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {Cost-efficient quality assurance of natural language processing tools through continuous monitoring with continuous integration}, series = {3rd International Workshop on Software Engineering Research and Industrial Practice}, booktitle = {3rd International Workshop on Software Engineering Research and Industrial Practice}, doi = {10.1145/2897022.2897029}, pages = {46 -- 52}, year = {2016}, language = {en} } @misc{Schreiber2016, author = {Schreiber, Marc}, title = {Mit Maximum-Entropie das Parsing nat{\"u}rlicher Sprache erlernen}, publisher = {FH Aachen}, address = {Aachen}, pages = {23 Seiten}, year = {2016}, abstract = {F{\"u}r die Verarbeitung von nat{\"u}rlicher Sprache ist ein wichtiger Zwischenschritt das Parsing, bei dem f{\"u}r S{\"a}tze der nat{\"u}rlichen Sprache Ableitungsb{\"a}ume bestimmt werden. Dieses Verfahren ist vergleichbar zum Parsen formaler Sprachen, wie z. B. das Parsen eines Quelltextes. Die Parsing-Methoden der formalen Sprachen, z. B. Bottom-up-Parser, k{\"o}nnen nicht auf das Parsen der nat{\"u}rlichen Sprache {\"u}bertragen werden, da keine Formalisierung der nat{\"u}rlichen Sprachen existiert [3, 12, 23, 30]. In den ersten Programmen, die nat{\"u}rliche Sprache verarbeiten [32, 41], wurde versucht die nat{\"u}rliche Sprache mit festen Regelmengen zu verarbeiten. Dieser Ansatz stieß jedoch schnell an seine Grenzen, da die Regelmenge nicht vollst{\"a}ndig sowie nicht minimal ist und wegen der ben{\"o}tigten Menge an Regeln schwer zu verwalten ist. Die Korpuslinguistik [22] bot die M{\"o}glichkeit, die Regelmenge durch Supervised-Machine-Learning-Verfahren [2] abzul{\"o}sen. Teil der Korpuslinguistik ist es, große Textkorpora zu erstellen und diese mit sprachlichen Strukturen zu annotieren. Zu diesen Strukturen geh{\"o}ren sowohl die Wortarten als auch die Ableitungsb{\"a}ume der S{\"a}tze. Vorteil dieser Methodik ist es, dass repr{\"a}sentative Daten zur Verf{\"u}gung stehen. Diese Daten werden genutzt, um mit Supervised-Machine-Learning-Verfahren die Gesetzm{\"a}ßigkeiten der nat{\"u}rliche Sprachen zu erlernen. Das Maximum-Entropie-Verfahren ist ein Supervised-Machine-Learning-Verfahren, das genutzt wird, um nat{\"u}rliche Sprache zu erlernen. Ratnaparkhi [25] nutzt Maximum-Entropie, um Ableitungsb{\"a}ume f{\"u}r S{\"a}tze der nat{\"u}rlichen Sprache zu erlernen. Dieses Verfahren macht es m{\"o}glich, die nat{\"u}rliche Sprache (abgebildet als Σ∗) trotz einer fehlenden formalen Grammatik zu parsen.}, language = {de} } @inproceedings{SchreiberKraftZuendorf2017, author = {Schreiber, Marc and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {Metrics Driven Research Collaboration: Focusing on Common Project Goals Continuously}, series = {39th International Conference on Software Engineering, May 20-28, 2017 - Buenos Aires, Argentina}, booktitle = {39th International Conference on Software Engineering, May 20-28, 2017 - Buenos Aires, Argentina}, pages = {8 Seiten}, year = {2017}, abstract = {Research collaborations provide opportunities for both practitioners and researchers: practitioners need solutions for difficult business challenges and researchers are looking for hard problems to solve and publish. Nevertheless, research collaborations carry the risk that practitioners focus on quick solutions too much and that researchers tackle theoretical problems, resulting in products which do not fulfill the project requirements. In this paper we introduce an approach extending the ideas of agile and lean software development. It helps practitioners and researchers keep track of their common research collaboration goal: a scientifically enriched software product which fulfills the needs of the practitioner's business model. This approach gives first-class status to application-oriented metrics that measure progress and success of a research collaboration continuously. Those metrics are derived from the collaboration requirements and help to focus on a commonly defined goal. An appropriate tool set evaluates and visualizes those metrics with minimal effort, and all participants will be pushed to focus on their tasks with appropriate effort. Thus project status, challenges and progress are transparent to all research collaboration members at any time.}, language = {en} } @inproceedings{SchreiberKraftZuendorf2017, author = {Schreiber, Marc and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {Metrics driven research collaboration: focusing on common project goals continuously}, series = {Proceedings : 2017 IEEE/ACM 4th International Workshop on Software Engineering Research and Industrial Practice : SER\&IP 2017 : 21 May 2017 Buenos Aires, Argentina}, booktitle = {Proceedings : 2017 IEEE/ACM 4th International Workshop on Software Engineering Research and Industrial Practice : SER\&IP 2017 : 21 May 2017 Buenos Aires, Argentina}, editor = {Bilof, Randall}, publisher = {IEEE Press}, address = {Piscataway, NJ}, isbn = {978-1-5386-2797-6}, doi = {10.1109/SER-IP.2017..6}, pages = {41 -- 47}, year = {2017}, language = {en} } @inproceedings{SchmidtsBoltesKraftetal.2017, author = {Schmidts, Oliver and Boltes, Maik and Kraft, Bodo and Schreiber, Marc}, title = {Multi-pedestrian tracking by moving Bluetooth-LE beacons and stationary receivers}, series = {2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 18-21 September 2017, Sapporo, Japan}, booktitle = {2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 18-21 September 2017, Sapporo, Japan}, pages = {1 -- 4}, year = {2017}, language = {en} } @inproceedings{SchreiberKraftZuendorf2018, author = {Schreiber, Marc and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {NLP Lean Programming Framework: Developing NLP Applications More Effectively}, series = {Proceedings of NAACL-HLT 2018: Demonstrations, New Orleans, Louisiana, June 2 - 4, 2018}, booktitle = {Proceedings of NAACL-HLT 2018: Demonstrations, New Orleans, Louisiana, June 2 - 4, 2018}, doi = {10.18653/v1/N18-5001 }, pages = {5 Seiten}, year = {2018}, abstract = {This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom natural language processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf's properties developers and domain experts are able to build domain-specific NLP applications more efficiently. NLPf is Opensource software and available at https:// gitlab.com/schrieveslaach/NLPf.}, language = {en} } @inproceedings{SchmidtsKraftSchreiberetal.2018, author = {Schmidts, Oliver and Kraft, Bodo and Schreiber, Marc and Z{\"u}ndorf, Albert}, title = {Continuously evaluated research projects in collaborative decoupled environments}, series = {2018 ACM/IEEE 5th International Workshop on Software Engineering Research and Industrial PracticePractice, May 29, 2018, Gothenburg, Sweden : SER\&IP' 18}, booktitle = {2018 ACM/IEEE 5th International Workshop on Software Engineering Research and Industrial PracticePractice, May 29, 2018, Gothenburg, Sweden : SER\&IP' 18}, publisher = {ACM}, address = {New York, NY}, pages = {1 -- 9}, year = {2018}, abstract = {Often, research results from collaboration projects are not transferred into productive environments even though approaches are proven to work in demonstration prototypes. These demonstration prototypes are usually too fragile and error-prone to be transferred easily into productive environments. A lot of additional work is required. Inspired by the idea of an incremental delivery process, we introduce an architecture pattern, which combines the approach of Metrics Driven Research Collaboration with microservices for the ease of integration. It enables keeping track of project goals over the course of the collaboration while every party may focus on their expert skills: researchers may focus on complex algorithms, practitioners may focus on their business goals. Through the simplified integration (intermediate) research results can be introduced into a productive environment which enables getting an early user feedback and allows for the early evaluation of different approaches. The practitioners' business model benefits throughout the full project duration.}, language = {en} }