Burcu Can
Scholar

Burcu Can

Google Scholar ID: BalzcSEAAAAJ
University of Stirling
Natural Language Processing
Citations & Impact
All-time
Citations
581
 
H-index
13
 
i10-index
16
 
Publications
20
 
Co-authors
27
list available
Contact
No contact links provided.
Resume (English only)
Academic Achievements
  • Paper 'Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments' accepted to LREC-Coling 2024 (March 2024)
  • Paper 'Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels' accepted to EACL 2024 (March 2024)
  • Project 'Joint learning of morphology and syntax in Turkish' awarded TUBITAK Project Performance Award (Grant 115E464, January 2022)
  • Released Turkish Neural NLP Toolkit (December 2021)
  • Best Paper Award at RepL4NLP workshop (ACL 2018) for 'Characters or Morphemes: How to Represent Words?'
  • Appointed Senior Associate Editor for ACM Transactions on Asian and Low-Resource Language Information Processing (July 2024)
  • Associate Editor for Journal of Natural Language Engineering (JNLE)
  • Editorial board member of Turkish Journal of Electrical Engineering & Computer Sciences
  • Organized the 8th Workshop on Representation Learning for NLP (Repl4NLP) at ACL 2023
Research Experience
  • Assistant Professor at Hacettepe University (2015–2020)
  • Reader in Computational Linguistics at the University of Wolverhampton (2020–2022)
  • Visiting researcher at the University of York (2014)
  • Visiting researcher at the Institute of Statistical Mathematics, Tokyo (2019)
  • Currently Lecturer in Computing Science at the University of Stirling
Background
  • Lecturer in Computer Science at the Department of Computing Science and Mathematics, University of Stirling, Scotland
  • Member of the Data Science and Intelligent Systems Research Group
  • Research focuses on Natural Language Processing using unsupervised learning techniques, particularly nonparametric Bayesian methods
  • Specific interests include morphology, syntax, and semantics
  • Also explores deep learning for representation learning in agglutinative languages