Acar, Ali

Profile URL
https://hdl.handle.net/11147/16253
Name Variants
Job Title
Email Address
Main Affiliation
01. Izmir Institute of Technology
Status
Current Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

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SDG data is not available
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Scholarly Output

3

Articles

2

Views / Downloads

8095/221

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

1

Scopus Citation Count

1

Patents

0

Projects

0

WoS Citations per Publication

0.33

Scopus Citations per Publication

0.33

Open Access Source

2

Supervised Theses

1

JournalCount
ACM Transactions on Asian and Low-Resource Language Information Processing1
International Journal of Software Engineering and Knowledge Engineering1
Current Page: 1 / 1

Scopus Quartile Distribution

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Author of Publication: search.filters.isAuthorOfPublication.bd1d2d24-79ff-4a37-b824-c8f2888a1389

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Article
    Recognition of Counterfactual Statements in Turkish
    (Assoc Computing Machinery, 2025) Acar, Ali; Tekir, Selma; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    Counterfactual statements are examples of causal reasoning as they describe events that did not happen and, optionally, those events' consequences if they happened. SemEval-2020 introduces the counterfactual detection (CFD) task and shares an English dataset. Since then, a set of datasets has been released in English, German, and Japanese as part of Amazon product reviews. This work releases the first Turkish corpus of counterfactuals (TRCD). The data collection process is driven by a clue phrase list of counterfactuals, mainly in the form of verb inflections in Turkish. We use clue phrase-based filtering to collect sentences from the Turkish National Corpus (TNC). On the other hand, half of the collection is subject to random word filtering to avoid selection bias due to clue phrases. After the human annotation process with an Inter Annotator Agreement of 0.65, we have 5000 sentences, of which 12.8% contain counterfactual statements. Furthermore, we provide a comprehensive baseline of transformer-based models by testing the effect of clue phrases, cross-lingual performance comparisons using the available CFD datasets, and zero-shot cross-lingual classification experiments using fine-tuning on the different combinations of the existing datasets. The results confirm that TRCD is compatible with the other CFD datasets. Moreover, fine-tuning a Turkish-specific model (BERTurk) performs better than the multilingual alternatives (mBERT and XLM-R). BERTurk is more robust to clue phrase masking. This result emphasizes the importance of a language-specific tokenizer for contextual understanding, especially for low-resource languages. Finally, our qualitative analysis gives insights into errors by different models.
  • Master Thesis
    Recognition of Counterfactual Statements in Turkish
    (01. Izmir Institute of Technology, 2023) Tekir, Selma; Acar, Ali; Tekir, Selma; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    Counterfactual statements describe an event that did not happen or cannot happen, and optionally the consequence of this event if it would happen. Counterfactual statements are the building blocks of human thought processes as people constantly reflect upon past happenings and consider their future implications. Counterfactual reasoning is essential for machine intelligence and explainable artificial intelligence studies. Detecting counterfactuals automatically with machine learning algorithms is very crucial for these areas. This thesis presents the development of the first-ever Turkish counterfactual detection dataset. It presents a comprehensive classification baseline and expands the scope of counterfactual detection to include the Turkish language.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content
    (World Scientific Publishing, 2021) Sezerer, Erhan; Tenekeci, Samet; Tekir, Selma; Baloğlu, Bora; Tekir, Selma; Acar, Ali; Tenekeci, Samet; Sezerer, Erhan; Baloğlu, Bora; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    In the field of software engineering, practitioners' share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors' mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves 63.8% accuracy in binary classification in SO design patterns tag and 71.6% accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system's predicted reputation labels match the quality of provided answers.