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Inhalt des Dokuments

Neslihan Iskender

Lupe [1]

Research Group

Crowdsourcing and Open Data [2]

 

Teaching

  • Study Project Quality & Usability (Since SS 2018)
  • Interdiziplinäres Medienprojekt (Since SS 2018)
  • Usability Engineering (Exercise SS 2018)

 

Biography

Neslihan Iskender received her Bachelor and Master of Science degree in Industrial Engineering and Management at the Karlsruhe Institute of Technology. During her studies, she focused on managing new technologies and innovation management. Since May 2017, she is employed as a research assistant at the Quality and Usability Labs where she is working towards a PhD in the field of crowdsourcing. Her research Topics are:

  • Crowd assessments: Usability, UX, QoE, Quality
  • Real-time interaction, human computation as a service, (HuaaS)
  • Hybrid Worfklows for micro-task crowdsourcing
  • Internal Crowdsourcing

 

Current Projects

  • DEKA -  Design und Entwicklung einer kollaborativen digitalen Arbeitsplattform für die Digitalisierung von Innovationsprozessen  [3]

 

Past Projects

  • ERICS – European Refugee Information and Communication Service (EIT-Digital, Project Lead) [4]
  • OurPuppet: Pflegeunterstützung mit einer interaktiven Puppe für informell Pflegende (BMBF [5])
  • ICU - Internes Crowdsourcing in Unternehmen: Arbeitnehmergerechte Prozessinnovationen durch digitale Beteiligung von Mitarbeiter/innen (BMBF) [6]

 

Contact

E-Mail: neslihan.iskender@tu-berlin.de

Phone: +49 (30) 8353-58347 

Fax: +49 (30) 8353-58409 

 

Address

Quality and Usability Lab

Deutsche Telekom Laboratories

Technische Universität Berlin

Ernst-Reuter-Platz 7

D-10587 Berlin, Germany 

 

 

Publications

2021

Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian (2021). Reliability of Human Evaluation for Text Summarization: Lessons Learned and Challenges Ahead [10]. Proceedings of the Workshop on Human Evaluation of NLP Systems. Association for Computational Linguistics, 86–96.

Link zur Publikation [11] Link zur Originalpublikation [12]

Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian (2021). Towards Hybrid Human-Machine Workflow for Natural Language Generation [13]. Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing. Association for Computational Linguistics, 1–7.

Link zur Publikation [14] Link zur Originalpublikation [15]

Iskender, Neslihan and Polzehl, Tim (2021). An Empirical Analysis of an Internal Crowdsourcing Platform: IT Implications for Improving Employee Participation [16]. Internal Crowdsourcing in Companies: Theoretical Foundations and Practical Applications. Springer International Publishing, 103–134.

Link zur Publikation [17] Link zur Originalpublikation [18]

Wedel, Marco and Ulbrich, Hannah and Pohlisch, Jakob and Göll, Edgar and Uhl, André and Iskender, Neslihan and Polzehl, Tim and Schröter, Welf and Porth, Florian (2021). Internes Crowdsourcing in Unternehmen [19]. Arbeit in der digitalisierten Welt: Praxisbeispiele und Gestaltungslösungen aus dem BMBF-Förderschwerpunkt. Springer Berlin Heidelberg, 335–349.

Link zur Publikation [20] Link zur Originalpublikation [21]

2020

Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian (2020). Best Practices for Crowd-based Evaluation of German Summarization: Comparing Crowd, Expert and Automatic Evaluation [22]. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems. Association for Computational Linguistics (ACL), 164–175.

Link zur Publikation [23] Link zur Originalpublikation [24]

Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian (2020). Towards a Reliable and Robust Methodology for Crowd-Based Subjective Quality Assessment of Query-Based Extractive Text Summarization [25]. Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association (ELRA), 245–253.

Link zur Publikation [26] Link zur Originalpublikation [27]

Iskender, Neslihan and Polzehl, Tim and Möller, Sebastian (2020). Crowdsourcing versus the laboratory: towards crowd-based linguistic text quality assessment of query-based extractive summarization [28]. Proceedings of the Conference on Digital Curation Technologies (Qurator 2020). CEUR, 1–16.

Link zur Publikation [29] Link zur Originalpublikation [30]

2019

Iskender, Neslihan and Gabryszak, Aleksandra and Polzehl, Tim and Hennig, Leonhard and Möller, Sebastian (2019). A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries [31]. 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 1–3.

Link zur Publikation [32]

Mittag, Gabriel and Liedtke, Louis and Iskender, Neslihan and Naderi, Babak and Hübschen, Tobias and Schmidt, Gerhard and Möller, Sebastian (2019). Einfluss der Position und Stimmhaftigkeit von verdeckten Paketverlusten auf die Sprachqualität [33]. Fortschritte der Akustik - DAGA 2019. Deutsche Gesellschaft für Akustik DEGA e.V., 950–953.

Link zur Publikation [34]

Publications

2018

Barz, Michael and Büyükdemircioglu, Neslihan and Prasad Surya, Rikhu and Polzehl, Tim and Sonntag, Daniel (2018). Device-Type Influence in Crowd-based Natural Language Translation Tasks [38]. Proceedings of the 1st Workshop on Subjectivity, Ambiguity and Disagreement (SAD) in Crowdsourcing 2018, and the 1st Workshop CrowdBias'18: Disentangling the Relation Between Crowdsourcing and Bias Management, 93–97.

Link zur Publikation [39] Link zur Originalpublikation

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