Kadir Bulut Ozler


2023

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clulab at MEDIQA-Chat 2023: Summarization and classification of medical dialogues
Kadir Bulut Ozler | Steven Bethard
Proceedings of the 5th Clinical Natural Language Processing Workshop

Clinical Natural Language Processing has been an increasingly popular research area in the NLP community. With the rise of large language models (LLMs) and their impressive abilities in NLP tasks, it is crucial to pay attention to their clinical applications. Sequence to sequence generative approaches with LLMs have been widely used in recent years. To be a part of the research in clinical NLP with recent advances in the field, we participated in task A of MEDIQA-Chat at ACL-ClinicalNLP Workshop 2023. In this paper, we explain our methods and findings as well as our comments on our results and limitations.

2020

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Fine-tuning for multi-domain and multi-label uncivil language detection
Kadir Bulut Ozler | Kate Kenski | Steve Rains | Yotam Shmargad | Kevin Coe | Steven Bethard
Proceedings of the Fourth Workshop on Online Abuse and Harms

Incivility is a problem on social media, and it comes in many forms (name-calling, vulgarity, threats, etc.) and domains (microblog posts, online news comments, Wikipedia edits, etc.). Training machine learning models to detect such incivility must handle the multi-label and multi-domain nature of the problem. We present a BERT-based model for incivility detection and propose several approaches for training it for multi-label and multi-domain datasets. We find that individual binary classifiers outperform a joint multi-label classifier, and that simply combining multiple domains of training data outperforms other recently-proposed fine tuning strategies. We also establish new state-of-the-art performance on several incivility detection datasets.