1.8k citations · 2.2k across the 17 of their papers we have counts for
10 papers · 1 filter
Hate Speech Detection in Turkish and Arabic: A Comprehensive Study
Somaiyeh Dehghan, Gökçe Uludoğan, Mehmet Umut Şen +3
Online hate speech has been linked to a global rise in violence against minorities, including incidents such as mass shootings, lynchings, and ethnic cleansing. Societies grappling…
Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods
Burak Suyunu, Enes Taylan, Arzucan Özgür
Tokenization is a crucial step in processing protein sequences for machine learning models, as proteins are complex sequences of amino acids that require meaningful segmentation to…
Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text
Hasin Rehana, Nur Bengisu Çam, Mert Basmaci +5
Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedi…
Enhancements to the BOUN Treebank Reflecting the Agglutinative Nature of Turkish
Büşra Marşan, Salih Furkan Akkurt, Muhammet Şen +7
In this study, we aim to offer linguistically motivated solutions to resolve the issues of the lack of representation of null morphemes, highly productive derivational processes, a…
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts
M. Melih Mutlu, Arzucan Özgür
Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility…
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Yi Huang, Buse Giledereli, Abdullatif Köksal +2
Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Res…
Cluster-based Mention Typing for Named Entity Disambiguation
Arda Çelebi, Arzucan Özgür
An entity mention in text such as "Washington" may correspond to many different named entities such as the city "Washington D.C." or the newspaper "Washington Post." The goal of na…
A Hybrid Approach to Dependency Parsing: Combining Rules and Morphology with Deep Learning
Şaziye Betül Özateş, Arzucan Özgür, Tunga Güngör +1
Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However,…
Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool
Utku Türk, Furkan Atmaca, Şaziye Betül Özateş +6
In this paper, we introduce the resources that we developed for Turkish dependency parsing, which include a novel manually annotated treebank (BOUN Treebank), along with the guidel…
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
Eda Okur, Hakan Demir, Arzucan Özgür
Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant atten…