4 citations · 9 across the 6 of their papers we have counts for
6 papers
Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks
Savas Yildirim
Deep learning-based and lately Transformer-based language models have been dominating the studies of natural language processing in the last years. Thanks to their accurate and fas…
Few-shot learning for sentence pair classification and its applications in software engineering
Robert Kraig Helmeczi, Mucahit Cevik, Savas Yıldırım
Few-shot learning-the ability to train models with access to limited data-has become increasingly popular in the natural language processing (NLP) domain, as large language models…
Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data
Savas Yildirim, Mucahit Cevik, Devang Parikh +1
The analysis of software requirement specifications (SRS) using Natural Language Processing (NLP) methods has been an important study area in the software engineering field in rece…
Transfer learning for conflict and duplicate detection in software requirement pairs
Garima Malik, Savas Yildirim, Mucahit Cevik
Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software devel…
A Prompt-based Few-shot Learning Approach to Software Conflict Detection
Robert K. Helmeczi, Mucahit Cevik, Savas Yıldırım
A software requirement specification (SRS) document is an essential part of the software development life cycle which outlines the requirements that a software program in developme…
Token Classification for Disambiguating Medical Abbreviations
Mucahit Cevik, Sanaz Mohammad Jafari, Mitchell Myers +1
Abbreviations are unavoidable yet critical parts of the medical text. Using abbreviations, especially in clinical patient notes, can save time and space, protect sensitive informat…