Reyyan Yeniterzi


2021

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Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report
Ali Hürriyetoğlu | Hristo Tanev | Vanni Zavarella | Jakub Piskorski | Reyyan Yeniterzi | Osman Mutlu | Deniz Yuret | Aline Villavicencio
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of socio-political events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.

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SU-NLP at CASE 2021 Task 1: Protest News Detection for English
Furkan Çelik | Tuğberk Dalkılıç | Fatih Beyhan | Reyyan Yeniterzi
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

This paper summarizes our group’s efforts in the multilingual protest news detection shared task, which is organized as a part of the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) Workshop. We participated in all four subtasks in English. Especially in the identification of event containing sentences task, our proposed ensemble approach using RoBERTa and multichannel CNN-LexStem model yields higher performance. Similarly in the event extraction task, our transformer-LSTM-CRF architecture outperforms regular transformers significantly.

2020

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SU-NLP at WNUT-2020 Task 2: The Ensemble Models
Kenan Fayoumi | Reyyan Yeniterzi
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)

In this paper, we address the problem of identifying informative tweets related to COVID-19 in the form of a binary classification task as part of our submission for W-NUT 2020 Task 2. Specifically, we focus on ensembling methods to boost the classification performance of classification models such as BERT and CNN. We show that ensembling can reduce the variance in performance, specifically for BERT base models.

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Event Clustering within News Articles
Faik Kerem Örs | Süveyda Yeniterzi | Reyyan Yeniterzi
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020

This paper summarizes our group’s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.

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SU-NLP at SemEval-2020 Task 12: Offensive Language IdentifiCation in Turkish Tweets
Anil Ozdemir | Reyyan Yeniterzi
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper summarizes our group’s efforts in the offensive language identification shared task, which is organized as part of the International Workshop on Semantic Evaluation (Sem-Eval2020). Our final submission system is an ensemble of three different models, (1) CNN-LSTM, (2) BiLSTM-Attention and (3) BERT. Word embeddings, which were pre-trained on tweets, are used while training the first two models. BERTurk, which is the first BERT model for Turkish, is also explored. Our final submitted approach ranked as the second best model in the Turkish sub-task.

2011

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Exploiting Morphology in Turkish Named Entity Recognition System
Reyyan Yeniterzi
Proceedings of the ACL 2011 Student Session

2010

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Syntax-to-Morphology Mapping in Factored Phrase-Based Statistical Machine Translation from English to Turkish
Reyyan Yeniterzi | Kemal Oflazer
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Transforming Controlled Natural Language Biomedical Queries into Answer Set Programs
Esra Erdem | Reyyan Yeniterzi
Proceedings of the BioNLP 2009 Workshop