Bereichsbild

 

Dummy

 

HITS und Neuphilologische Fakultät
HITS und Institut für Computerlinguistik

Ruprecht-Karls-Universität Heidelberg
 

Kontakt:

michael.strube@h-its.org


 

Prof. Dr. Michael Strube

Fellow-Klasse 2019-20

 

Forschungsgebiete:

  • Automatic discourse processing
  • Coreference resolution
  • Coherence modeling
  • Automatic summarization
  • Ethics in NLP

Lebenslauf

Positionen

  • 2017-2018 Institutssprecher HITS
  • seit 2010 Honorarprofessor am Institut für Computerlinguistik, UHD
  • seit 2001 Gruppenleiter „Natural Language Processing“ am Heidelberger Institut für Theoretische Studien (vormals EML Research und EML)
  • 2000 wissenschaftlicher Mitarbeiter EML
  • 1999-2000 Postdoktorand am Institute for Language, Cognition and Computation, University of Edinburgh
  • 1997-1999 Postdoctoral Fellow am Institute for Research in Cognitive Science an der University of Pennsylvania
  • 1992-1996 Doktorand Computerlinguistik/Linguistische Informatik an der Universität  Freiburg
  • 1986-1992 Studium der Neueren Deutschen Literaturgeschichte und der Philosophie an der Universität Freiburg

Other professional activitites:

  • 2017, 2018 Program Co-Chair Workshops on Ethics in NLP at *ACL
  • 2015 Program Co-Chair Annual Meeting of the Association for

Computational Linguistics (ACL 2015)

  • 2013 General Co-Chair SIGdial Conference
  • 2011 Program Co-Chair AI and Web Track at AAAI 2011
  • 2004 Program Co-Chair SIGdial Workshop

 

Arbeitsvorhaben

Does the quality of writing influence scientific impact?

The importance and quality of a new discovery presented in a scientific article is often evaluated by the number of its citations. It is unclear, however, in how far the quality of the writing influences the impact of scientific articles. Since the impact factor is of vital importance not only for the advance of science, but also for the career of scientists, we want to explore whether a non-scientific factor – the quality of the writing – influences the reach and popularity of scientific articles.

Our hypothesis is that an article that is easy and enjoyable to read and that contains stylistic features that attract readers’ attention is read and cited more often than an article hard to follow and written in a less attractive style, even if the scientific results are of comparable quality and importance. On the basis of a comprehensive set of articles published in a variety of scientific journals (chosen and analyzed by Frauke Gräter), we want to explore whether the quality of the writing correlates with the number of citations or other metrics of impact. We will follow two lines of research: a high-throughput machine learning approach to explore the degree of readability and coherence of the text (Michael Strube), and a detailed analysis of the stylistic features of exemplary articles (Vera Nünning).

Our project aims to stimulate an informed discussion about the significance of the quality of writing for the creation of successful scientific texts.

 

Ausgewählte Publikationen:

  • Hou, Y., Markert, K., and Strube, M. Unrestricted bridging resolution. Computational Linguistics, 44(2), 2018, pp.237-284.
  • Mesgar, M. and Strube, M. A neural local coherence model for text quality assessment. In Proc. EMNLP 2018, to appear.
  • Moosavi, N.S. and Strube, M. Using linguistic features to improve the generalization capability of neural coreference resolvers. In Proc. EMNLP 2018, to appear.
  • Martschat, S. and Strube, M. Latent structures for coreference resolution. Transactions of the Association for Computational Linguistics, 3:405-418, 2015.
  • Nastase, V. and Strube, M. Transforming Wikipedia into a large scale multilingual concept network. Artificial Intelligence, 194:62-85, 2013.
  • Guinaudeau, C. and Strube, M. Graph-based local coherence modeling. In Proc. ACL 2013, pp.93-103
  • Ponzetto, S.P. and Strube, M. Taxonomy induction based on a collaboratively built knowledge repository. Artificial Intelligence, 175: 1737-1756, 2011.
  • Ponzetto, S.P. and Strube, M. Knowledge derived from Wikipedia for computing semantic relatedness. Journal for Artificial Intelligence Research, 30:181-212, 2007.
  • Eckert, M. and Strube, M. Dialogue acts, synchronising units and anaphora resolution. Journal of Semantics, 17(1): 51-89, 2001.
  • Strube, M. and Hahn, U. Functional centering: Grounding referential coherence in information structure. Computational Linguistics, 25(3): 309-344, 1999.
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