Papers by Arzucan Özgür
Dealing with Data Scarcity in Spoken Question Answering (2024.lrec-main)
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| Challenge: | erroneous automatic speech recognition transcriptions and data scarcity hinder spoken QA models . paper focuses on using limited annotated data to improve spoken qa performance . |
| Approach: | They propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. |
| Outcome: | The proposed model produces question-answer pairs from unannotated data with 5.5% relative gain over the model trained with annotated datasets. |
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution (2021.emnlp-main)
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| Challenge: | Multi-label text classification is a challenging task because it requires capturing label dependencies. |
| Approach: | They propose to use distribution-balanced loss functions to solve label dependency problems in multi-label text classification by capturing label dependencies from a fixed-set of labels. |
| Outcome: | The proposed loss function addresses both the class imbalance and label linkage problems and outperforms other loss functions. |
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes (D19-57)
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| Challenge: | In 2011, the Bacteria Biotope Task was conducted for the first time as a part of the BioNLP Shared Task targeting the extraction of useful information regarding bacteria and their habitats. |
| Approach: | They propose two systems for the normalization of entities and the identification of relations between entities given a biomedical text. |
| Outcome: | The proposed method performs as good as deep learning based methods which require labeled data. |
Evaluating the Quality of a Corpus Annotation Scheme Using Pretrained Language Models (2024.lrec-main)
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Furkan Akkurt, Onur Gungor, Büşra Marşan, Tunga Gungor, Balkiz Ozturk Basaran, Arzucan Özgür, Susan Uskudarli
| Challenge: | Pretrained language models and large language models are increasingly used to assist in a variety of natural language processing tasks. |
| Approach: | They propose to use pretrained language models and large language models to evaluate their quality in natural language processing. |
| Outcome: | The proposed annotation scheme (2.11) yields sentences with higher success rate than the previous one. |
HATECAT-TR: A Hate Speech Span Detection and Categorization Dataset for Turkish (2025.findings-emnlp)
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| Challenge: | a new dataset of Turkish tweets contains 4465 hateful spans . each hateful post is directed at one of eight minority groups . |
| Approach: | They propose a span-annotated dataset of Turkish tweets containing 4465 hateful spans . each hateful spat is categorized into one of five discourse types . |
| Outcome: | The proposed dataset contains 4465 hateful spans across 2981 tweets . each span is categorized into one of five discourse types . |
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts (2022.acl-srw)
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| Challenge: | Sentiment analysis is a field that is growing due to the availability of the Internet and the growing number of online platforms. |
| Approach: | They propose an annotated Turkish dataset suitable for targeted sentiment analysis. |
| Outcome: | The proposed models outperform the traditional models for the targeted sentiment analysis task. |
TULAP - An Accessible and Sustainable Platform for Turkish Natural Language Processing Resources (2023.eacl-demo)
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Susan Uskudarli, Muhammet Şen, Furkan Akkurt, Merve Gürbüz, Onur Gungor, Arzucan Özgür, Tunga Güngör
| Challenge: | a growing interest in the field of natural language processing is resulting in applications solving NLP tasks. |
| Approach: | They propose to create an open-source platform to share Turkish NLP resources . they propose to use the platform to publish open-sourced Turkish Nlp resources based on a research lab's datasets and tools. |
| Outcome: | The proposed platform is easy-to-use and publishes open-source Turkish NLP resources. |
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (2022.findings-naacl)
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| Challenge: | Code-switching dependency parsing is a challenging task due to the scarcity of necessary resources and structural difficulties embedded in code-switch languages. |
| Approach: | They propose to use sequence labeling models as auxiliary tasks for code-switched dependency parsing in a semi-supervised scheme and acquire state-of-the-art scores on all studied languages. |
| Outcome: | The proposed model outperforms the previous model by 7.4 LAS points on average on all of the studied languages. |
A Framework for Automatic Generation of Spoken Question-Answering Data (2022.findings-emnlp)
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| Challenge: | Existing frameworks to automatically generate a spoken question answering dataset are limited by the amount of spoken text documents available. |
| Approach: | They propose to use QG module to generate questions from text documents, TTS module to convert text documents into spoken form and automatic speech recognition module to transcribe spoken content. |
| Outcome: | The proposed framework is efficient for automatically generating spoken QA datasets. |
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification (2020.findings-emnlp)
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| Challenge: | Current approaches for relation classification are focused on the English language and require lots of training data with human annotations. |
| Approach: | They propose a baseline model based on Multilingual BERT and a new multilingual pretraining setup . they propose 'relationship classification' models that use distant supervision . |
| Outcome: | The proposed model significantly improves the baseline model with distant supervision. |
A Dataset for Investigating the Impact of Context for Offensive Language Detection in Tweets (2023.findings-emnlp)
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| Challenge: | Offensive language detection is crucial in natural language processing . we investigated the importance of contextual information for detecting offensive language in tweets . |
| Approach: | They investigated the importance of contextual information for detecting offensive language in tweets . they used a Turkish tweet dataset with over 28,000 tweet-reply pairs . |
| Outcome: | The proposed model performs better with and without contextual information than with and with contextual information. |