Papers by Necva Bölücü
TurkishDelightNLP: A Neural Turkish NLP Toolkit (2022.naacl-demo)
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| Challenge: | a neural Turkish NLP toolkit performs computational linguistic analyses from morphological level to semantic level. |
| Approach: | They propose a neural Turkish NLP toolkit that performs computational linguistic analyses from morphological level to semantic level. |
| Outcome: | The proposed toolkit performs computational linguistic analyses from morphological level to semantic level in Turkish. |
MetaLead: A Comprehensive Human-Curated Leaderboard Dataset for Transparent Reporting of Machine Learning Experiments (2026.eacl-long)
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| Challenge: | Existing leaderboards capture only the best results from each paper and have limited metadata. |
| Approach: | They propose to create a fully human-annotated ML Leaderboard dataset that captures all experimental results and contains extra metadata. |
| Outcome: | The MetaLead dataset captures all experimental results and contains extra metadata for cross-domain evaluation. |
Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings (2025.findings-emnlp)
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| Challenge: | Existing evaluation methods for compressed text embeddings are either expensive or too simplistic. |
| Approach: | They propose a task-agnostic intrinsic evaluation framework that provides a reliable proxy for downstream performance. |
| Outcome: | The proposed framework provides a reliable proxy for downstream performance. |
Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SciLire System (2026.eacl-demo)
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Necva Bölücü, Jessica Irons, Changhyun Lee, Brian Jin, Maciej Rybinski, Huichen Yang, Andreas Duenser, Stephen Wan
| Challenge: | rapid growth of scientific literature has made manual extraction of structured knowledge increasingly impractical. |
| Approach: | They propose a system for creating datasets from scientific literature that integrates human-AI teaming principles and iterative workflows. |
| Outcome: | The proposed system improves extraction fidelity and facilitates efficient dataset creation. |
impact of sample selection on in-context learning for entity extraction from scientific writing (2023.findings-emnlp)
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| Challenge: | Prompt-based use of Large Language Models is becoming popular . specialized domains such as entity extraction are expensive to annotate . |
| Approach: | They propose to use a prompt set-up to provide training examples along with the inference request. |
| Outcome: | The proposed methods improve on a fully supervised transformer-based baseline. |
Turkish Universal Conceptual Cognitive Annotation (2022.lrec-1)
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| Challenge: | UCCA-annotated datasets have been released in English, French, and German . a semi-automatic annotation approach is used to annotate the datasets . |
| Approach: | They propose to use an external semantic parser to annotate Turkish sentences . they use the same parsers for evaluation purposes and conducted experiments . |
| Outcome: | The proposed dataset is the first UCCA-annotated Turkish dataset . the results show that the parser can improve on the initial annotations . |