Demir, Şeniz
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Demi̇r, Şeni̇z
Demir, Seniz
Demir, S
Demir, Seniz
Demir, S
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demirse@mef.edu.tr
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02.02. Department of Computer Engineering
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34
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507
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13
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14
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7
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4
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30
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49
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3
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3
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2.14
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| Journal | Count |
|---|---|
| 27th Signal Processing and Communications Applications Conference, SIU 2019 | 1 |
| -- 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- Istanbul; Isik University Sile Campus -- 211450 | 1 |
| ACM Transactions on Asian and Low-Resource Language Information Processing | 1 |
| Computer Speech & Language | 1 |
| Engineering Science and Technology, an International Journal | 1 |
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Article Citation - WoS: 3Citation - Scopus: 2A Benchmark Dataset for Turkish Data-To Generation(Elsevier, 2022) Demir, Şeniz; Öktem, SezaIn the last decades, data-to-text (D2T) systems that directly learn from data have gained a lot of attention in natural language generation. These systems need data with high quality and large volume, but unfortunately some natural languages suffer from the lack of readily available generation datasets. This article describes our efforts to create a new Turkish dataset (Tr-D2T) that consists of meaning representation and reference sentence pairs without fine-grained word alignments. We utilize Turkish web resources and existing datasets in other languages for producing meaning representations and collect reference sentences by crowdsourcing native speakers. We particularly focus on the generation of single-sentence biographies and dining venue descriptions. In order to motivate future Turkish D2T studies, we present detailed benchmarking results of different sequence-to-sequence neural models trained on this dataset. To the best of our knowledge, this work is the first of its kind that provides preliminary findings and lessons learned from the creation of a new Turkish D2T dataset. Moreover, our work is the first extensive study that presents generation performances of transformer and recurrent neural network models from meaning representations in this morphologically-rich language.Master Thesis Interviewster: A chatbot evaluating competency based interviews using transformer models(MEF Üniversitesi, 2022) Atıcı, Onur Emre; Demir, Şenizİşe alım, insan kaynaklarının en sözel ve iletişimsel alanlarından biridir. Bu departmanın insan faktörünün baskın olması nedeniyle yeniliğe açık olduğu kadar önyargıya da açık olan birçok yönü bulunmaktadır. Bu da yapay zeka teknolojilerindeki ilerlemeyle birlikte birçok inovasyon ihtiyacını (ve şansını) beraberinde getirmektedir. Bu çalışmada adayları karşılayan, bilgi toplayan (ad-soyad, iş durumu, bilgisayar bilgisi, eğitimi, hobileri gibi) ve geçmiş deneyimleri hakkında yetkinlik bazlı sorular sunan ve bu soruları doğru cevaplayabilmesi için onlara yardımcı olan "Interviewster" adlı bir sohbet robotru oluşturmaya odaklanılmaktadır. Bu sohbet robotu adayı karşılar ve konuşmayı başlatır, adaydan toplanan verileri kaydeder, yetkinlik bazlı görüşme yapar ve sinir ağları mimarileri ve transformer tabanlı teknolojileri kullanan doğal dil işleme teknikleri ile adayın gerekli yetkinliğe sahip olup olmadığına karar verir. Web üzerinde çalışmakta olan bu sohbet robotu Python ile kodlanmış ve Flask ile web'de yayınlanmış olup Mysql veritabanını kullanan bir Python çekirdeği üzerinde çalışmaktadır. Bu tezde ilk olarak mülakat uygulamaları tanıtılmakta ve yetkinlik bazlı mülakatların yöntem ve uygulamaları anlatılmaktadır. Sonrasında Interviewster olarak adlandırılan sohbet robotunun mimarisi, kullanılan teknolojiler, kütüphaneler ve makine öğrenmesi teknikleri, detayları verilerek açıklanmıştır. Son olarak da transformer tabanlı modeller olan BERT, DeBERTa ve ELECTRA modellerinin gerçek adayların yetkinlik bazlı mülakat sonuçlarına uygulandığı bir değerlendirme çalışmasının sonuçları detaylı olarak tartışılmıştır.Article Citation - WoS: 20Citation - Scopus: 28An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition(Elsevier, 2021) Makaroğlu, Didem; Demir, Şeniz; Aras, Gizem; Çakır, AltanNamed entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory (BiLSTM) and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.Conference Object Dil Modelleri ile Akademik Özet Üretimi(Institute of Electrical and Electronics Engineers Inc., 2025) Bektas, Busra; Gultekin, Ali Ozgun; Ozdemiroglu, Emre; Yilmaz, Zeynep; Dikici, Buse; Demir, SenizIn recent years, large language models have demonstrated extraordinary capabilities in natural language processing tasks. The integration of these models to text summarization has highlighted the need for evaluating varying model performances under a standardized benchmarking framework. In this study, the performance of different large language models in generating abstracts of scientific papers which has a common structure and unique language is compared through an extensive experimental analysis. The abstracts automatically generated by these models using prompt engineering were evaluated via various evaluation metrics based on content overlap and semantic similarity. The results that we obtained demonstrated the effectiveness of large language models in abstract generation. © 2025 Elsevier B.V., All rights reserved.Article Mention Detection in Turkish Coreference Resolution(Tubitak Scientific & Technological Research Council Turkey, 2024) Demir, Seniz; Akdag, Hanifi IbrahimA crucial step in understanding natural language is detecting mentions that refer to real-world entities in a text and correctly identifying their boundaries. Mention detection is commonly considered a preprocessing step in coreference resolution which is shown to be helpful in several language processing applications such as machine translation and text summarization. Despite recent efforts on Turkish coreference resolution, no standalone neural solution to mention detection has been proposed yet. In this article, we present two models designed for detecting Turkish mentions by using feed-forward neural networks. Both models extract all spans up to a fixed length from input text as candidates and classify them as mentions or not mentions. The models differ in terms of how candidate text spans are represented. The first model represents a span by focusing on its first and last words, whereas the representation also covers the preceding and proceeding words of a span in the second model. Mention span representations are formed by using contextual embeddings, part-of-speech embeddings, and named-entity embeddings of words in interest where contextual embeddings are obtained from pretrained Turkish language models. In our evaluation studies, we not only assess the impact of mention representation strategies on system performance but also demonstrate the usability of different pretrained language models in resolution task. We argue that our work provides useful insights to the existing literature and the first step in understanding the effectiveness of neural architectures in Turkish mention detection.Article Citation - WoS: 1Citation - Scopus: 4Turkish Data-To Generation Using Sequence-To Neural Networks(Assoc Computing Machinery, 2023) Demir, ŞenizEnd-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequenceto-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained fromthis study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages.Master Thesis Developing a Relation Extraction Model From English Wikipedia Articles for Information Boxes(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karabal, Kaan; Demir, ŞenizWith the beginning of internet era in the mid '90s, the source of knowledge started to change rapidly from written sources such as the well-known Enyclopedia Britannica to online sources. One of the first online sources of the free encyclopedia was called Interpedia would let anyone contribute by writing articles and submitting to the database to published which is very similar with "Wikipedia", today's most used online & free encyclopedia.Master Thesis The use of pretrained language models in sentiment analysis(MEF Üniversitesi, 2022) Yürütücü, Ömer Yiğit; Demir, ŞenizDoğal dil işleme, dil bilim ve yapay zekânın alt konularından biridir. Duygu analizi herhangi bir konuda bir metni öznel içeriğine göre sınıflandırma yapar. Genellikle bireylerin çeşitli platformlarda bir konu hakkında düşünce, duygu ya da tutumu gibi verileri irdelemek, analiz etmek ve yorumlamak amacıyla kullanılan yöntemlerden biridir. Sosyal medya paylaşımlarındaki artış bu platformlarda yapılan duygu analizi çalışmalarını da artırmıştır. Duygu analizi yapılırken farklı yöntemlerden yararlanılır. Makine öğrenmesi ve doğal dil işleme algoritmaları ile duygu tespiti ile sınıflandırma yapılır. Son yıllarda önceden eğitilmiş dil modelleri makine öğrenmesi metotlarıyla birlikte ya da tek başına kullanılmaya başlamıştır. Bu tezin amacı önceden eğitilmiş dil modelleri ile sosyal medya yorumlarında duygu analizinin varsayımsal avantajlarını test etmektir. Bu amaçla Twitterdaki Covid-19 ile ilgili tweetler için duygu analizi yapılmıştır. Önceden eğitilmiş dil modelleri kullanılarak duygu yoğunlukları tespit edilmiş ve sonuçları karşılaştırılmıştır. Analizlerde BERT, RoBERTa ve BERTweet'ten yararlanılmıştır. Sonuçlar, duygu analizi için NLP tekniklerinin diğer teknikler kadar başarılı olduğunu göstermektedir.Article Ön Eğitimli Dil Modelleriyle Duygu Analizi(İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü, 2023) Yürütücü, Ömer Yiğit; Demir, ŞenizDuygu analizi, çeşitli platformlarda bir konu hakkında düşünce, duygu ya da tutumu irdelemek, analiz etmek ve yorumlamak amacıyla kullanılan yöntemlerden biridir. Farklı konulardaki metinlerin öznel içeriklerine göre sınıflandırılabildiği duygu analizinde makine öğrenmesi ve derin öğrenme modellerinden sıklıkla faydalanılmaktadır.Bu çalışmada, önceden eğitilmiş dil modellerinden yararlanılarak Covid-19 tweet metinleri üzerinde duygu analizi yapılmıştır. Naive Bayes sınıflandırıcıya ek olarak BERT, RoBERTa ve BERTweet dil modelleri kullanılarak farklı sınıflandırıcılar eğitilmiş ve tweet veri kümesi üzerinde elde edilen sonuçlar kıyaslanmıştır. Bildiride aktarılan çalışmanın ileride bu alanda yürütülecek araştırmalara bir zemin oluşturacağı öngörülmektedir.Conference Object Does Prompt Engineering Help Turkish Named Entity Recognition?(Institute of Electrical and Electronics Engineers Inc., 2024) Pektezol, A.S.; Ulugergerli, A.B.; Öztoklu, V.; Demir, ŞenizThe extraction of entity mentions in a text (named entity recognition) has been traditionally formulated as a sequence labeling problem. In recent years, this approach has evolved from recognizing entities to answering formulated questions related to entity types. The questions, constructed as prompts, are used to elicit desired entity mentions and their types from large language models. In this work, we investigated prompt engineering in Turkish named entity recognition and studied two prompting strategies to guide pretrained language models toward correctly identifying mentions. In particular, we examined the impact of zero-shot and few-shot prompting on the recognition of Turkish named entities by conducting experiments on two large language models. Our evaluations using different prompt templates revealed promising results and demonstrated that carefully constructed prompts can achieve high accuracy on entity recognition, even in languages with complex morphology. © 2024 IEEE.
