Ahmet Yıldırım

• Developing software to adapt universal dependencies for semantic representation. 
• Applying state-of-the-art deep learning methods in semantic representation.

Background

• Ph.D. in Computer Science, Boğaziçi University, Computer Engineering (2020)
• MSc. in Software Engineering, Boğaziçi University (2007)
• BSc. in Computer Engineering, Karadeniz Technical University (2005)

Publications

• Ahmet Yıldırım and Dag Trygve Truslew Haug, Experiments in training transformer sequence-to-sequence DRS parsers, The 15th International Conference on Computational Semantics, IWCS 2023, Nancy, France
• Dag T. T. Haug, Jamie Y. Findlay, and Ahmet Yildirim, The long and the short of it: DRASTIC, a semantically annotated dataset containing sentences of more natural length, The 4th International, Workshop on Designing Meaning Representation, DMR2023, Nancy, France
• Dag Haug, Ahmet Yildirim, Kristin Hagen, Anders Nøklestad, Rules and neural nets for morphological tagging of Norwegian - Results and challenges. 24th Nordic Conference on Computational Linguistics (NoDaLiDa), 425–435, Tórshavn, Faroe Islands, 2023
• Jamie Y. Findlay, Saeedeh Salimifar, Ahmet Yıldırım, Dag T. T. Haug, Rule-based semantic interpretation for Universal Dependencies. Sixth Workshop on Universal Dependencies (UDW, GURT/SyntaxFest 2023), 47–57, Washington, D.C., ACL, 2023