User Interface for Turkish Word Network KeNet

Özçelik, Rıza; Uludoğan, Gökçe; Parlar, Selen; Bakay, Özge; Ergelen, Özlem; Yıldız, Olcay Taner

doi:10.1109/siu.2019.8806463

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Yet the tasks conducted on different language varieties demand agile models that can both be tuned for the needs of a specific tagset and work in the context of lowresourced and sparse data (Scherrer, 2021). Hidden Markov Model (HMM)-based taggers present this opportunity (Schmid, 1994(Schmid, , 1995Özçelik et al, 2019;Lyashevskaya and Afanasev, 2021). The other probabilistic tool used for part-of-speech tagging is conditional random fields (CRF) (Behera, 2017).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

2023

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Welcome to the 4th International Workshop on Computational Approaches to Historical Language Change (LChange'23) co-located with EMNLP 2023. LChange is held on December 6th, 2023, as a hybrid event with participation possible both virtually and on-site in Singapore.Characterizing the time-varying nature of language will have broad implications and applications in multiple fields including linguistics, artificial intelligence, digital humanities, computational cognitive and social sciences. In this workshop, we bring together the world's pioneers and experts in computational approaches to historical language change with a focus on digital text corpora. In doing so, this workshop carries out the triple goals of disseminating state-of-the-art research on diachronic modeling of language change, fostering cross-disciplinary collaborations, and exploring the fundamental theoretical and methodological challenges in this growing niche of computational linguistic research.In response to the call, we received 28 submissions. Each of them was carefully evaluated by at least two members of the Program Committee, whom we believed to be most appropriate for each paper. Based on the reviewers' feedback we accepted 17 full and short papers as oral or poster presentations. We had two distinguished keynote presentations: the first by Gemma Boleda (Research Professor in the Department of Translation and Language Sciences of the Universitat Pompeu Fabra, Spain) who presented a talk entitled "What does semantic change have to do with Hello Kitty? Referring as the source of change", and the second by Mario Giulianelli (a postdoctoral fellow at ETH Zurich) with the talk "Neural language models for word usage representation and analysis". Finally, we invited five EMNLP'23 Findings papers to be presented as posters, which are not included in the workshop proceedings.To further support the community, we offered five student scholarships to cover registration fees. We also offered mentoring for four young researchers on their research topic in the field of language change, either during the workshop or virtually.We hope that you will find the workshop papers insightful and inspiring. We would like to thank the keynote speakers for their stimulating talks, the authors of all papers for their interesting contributions, and the members of the Program Committee for their insightful reviews. Our special thanks go to the emergency reviewers who stepped in to provide their expertise. We also express our gratitude to the EMNLP 2023 workshop chairs for their kind assistance during the organization process. Finally, our thanks go to our gold sponsor iguanodon.ai, as well as the research project "Towards Computational Lexical Semantic Change Detection" (Swedish Research Council, contract 2018-01184) and the research program "Change is Key!" (Riksbankens Jubileumsfond, contract M21-0021).

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Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

2023

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“…Every knight and their place on the board is explicitly shown in Figure 3. The graph analyses provide important information to extract information from data presented in the form of networks such as computational networks [28][29][30], social networks [31][32][33][34], biological networks [35][36][37], word networks [38][39][40], infection networks [41,42]. One of the many analysis methods is clustering which divides the whole network into smaller relational clusters of the nodes.…”

Section: Introductionmentioning

confidence: 99%

6'ya 6 Tahta Üzeri̇nde at Kaplama Problemi̇ni̇ Çözmek İçi̇n Deneti̇msi̇z Maki̇ne Öğrenme Algori̇tmasi

Güldal

2021

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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Modülerlik, çizgelerden bilgi çıkarmak için, çok kullanılan bir makine öğrenimi algoritmasıdır. Modülerlik, özünde, ele alınan ağı daha küçük kümelere böler. Oluşturulan kümeler, aynı kümedeki düğümler arasındaki paylaşılan özellikleri vurgular. Bu çalışmada 6×6 at çizgesini modülerlik yöntemiyle analiz ederek 6 At Kaplama Probleminin (6-AKP) çözümlerini elde ettik. Araştırmamız 0,1 ile 2,0 arasındaki çözünürlükler için değişmektedir. Çözünürlük 1,2 için bulunan maksimum modülerlik puanı 0,318'dir. 0,3 ve 0,4 olmak üzere çözünürlükler, tüm çözümleri 8 at ile tanımladı. Ayrıca, 0,2 çözünürlükler için 8 at ve 0,2, 0,3 ve 0,4 çözünürlükler için 9, 10, 11, 12, 13 at ile bazı çözümler elde edilmektedir. Ayrıca, çözünürlük 0,3, 6-AKP çözümlerini bulmak için en verimli çözünürlüktür. Ayrıca, analizlerimiz gösterdi ki 0,2 çözünürlüğü, 6-AKP'nin 195 çözümünü daha fazla çözüm bulmak için en iyi çözünürlüktür. Son olarak, modülerlik yöntemi, 2253 çözüm arasından 0,5 çözünürlük için 61'den, 0,2 çözünürlük için 195'e kadar olan çözümleri çıkarıyor.

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Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features

Altintas

Tantuğ

2023

Intelligent Systems with Applications

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User Interface for Turkish Word Network KeNet

Cited by 6 publications

References 2 publications

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

6'ya 6 Tahta Üzeri̇nde at Kaplama Problemi̇ni̇ Çözmek İçi̇n Deneti̇msi̇z Maki̇ne Öğrenme Algori̇tmasi

Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features

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