Burcin Buket Ogul

• Leseanu, Anda; Hallac, Ibrahim Riza; Ogul, Burcin Buket & Ogul, Hasan (2025). Revisiting Automatic Essay Assessment: A Relative Approach. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. s. 41–52. doi: 10.1007/978-3-032-08462-0_4. Fulltekst i vitenarkiv Vis sammendrag

  Automatic essay assessment has remained a challenging problem in natural language processing (NLP) for over fifty years. Traditionally framed as a regression task that predicts examiner-assigned scores, this work instead adopts a relative assessment perspective. The task is redefined as pairwise ranking, where essays are compared using standard assessment criteria, but their exact grades are assumed to be unknown and ultimately unimportant. Three core contributions are presented. First, an architecture is proposed for pairwise essay ranking using labeled quality criteria, with two fusion strategies explored. Second, an experimental setup for relative ranking is redefined based on a benchmark dataset originally intended for absolute scoring. Third, empirical comparisons are reported across architectural components and design choices. Results indicate that data-level fusion using LLaMA-based embeddings and a gradient-boosted classifier achieves the highest accuracy. A model-level fusion approach with a Siamese neural network and probabilistic loss also yields competitive performance, highlighting its potential for further research. This relative assessment framework offers practical value for student progress tracking and norm-referenced grading in educational contexts.

• Ogul, Burcin Buket & Özdemir, Suat (2022). A Pairwise Deep Ranking Model for Relative Assessment of Parkinson’s Disease Patients From Gait Signals. IEEE Access. 10, s. 6676–6683. doi: 10.1109/ACCESS.2021.3136724. Fulltekst i vitenarkiv Vis sammendrag

  Continuous monitoring of the symptoms is crucial to improve the quality of life for patients with Parkinson’s Disease (PD). Thus, it is necessary to objectively assess the PD symptoms. Since manual assessment is subjective and prone to misinterpretation, computer-aided methods that use sensory measurements have recently been used to make objective PD assessment. Current methods follow an absolute assessment strategy, where the symptoms are classified into known categories or quantified with exact values. These methods are usually difficult to generalize and considered to be unreliable in practice. In this paper, we formulate the PD assessment problem as a relative assessment of one patient compared to another. For this assessment, we propose a new approach to the comparative analysis of gait signals obtained via foot-worn sensors. We introduce a novel pairwise deep-ranking model that is fed by data from a pair of patients, where the data is obtained from multiple ground reaction force sensors. The proposed model, called Ranking by Siamese Recurrent Network with Attention , takes two multivariate time-series as inputs and produces a probability of the first signal having a higher continuous attribute than the second one. In ten-fold cross-validation, the accuracy of pairwise ranking predictions can reach up to 82% with an AUROC of 0.89. The model outperforms the previous methods for PD monitoring when run in the same experimental setup. To the best of our knowledge, this is the first study that attempts to relatively assess PD patients using a pairwise ranking measure on sensory data. The model can serve as a complementary model to computer-aided prognosis tools by monitoring the progress of the patient during the applied treatment.

Se alle arbeider i NVA

• Samarakoon, Pubudu Saneth; Ogul, Burcin Buket & Razick, Sabry (2024). Workshop: AI Tools with Biomedical Sciences (Norwegian AI Cloud). Fulltekst i vitenarkiv
• Razick, Sabry & Ogul, Burcin Buket (2024). ParaView Workshop. Fulltekst i vitenarkiv

Se alle arbeider i NVA