Tekir, Selma
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Name Variants
Tekir, S
Tekir, S.
Tekir, S.
Job Title
Email Address
selmatekir@iyte.edu.tr
Main Affiliation
03.04. Department of Computer Engineering
Status
Current Staff
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
2
Research Products
2ZERO HUNGER
0
Research Products
3GOOD HEALTH AND WELL-BEING
0
Research Products
4QUALITY EDUCATION
4
Research Products
5GENDER EQUALITY
0
Research Products
6CLEAN WATER AND SANITATION
0
Research Products
7AFFORDABLE AND CLEAN ENERGY
0
Research Products
8DECENT WORK AND ECONOMIC GROWTH
1
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
16
Research Products
10REDUCED INEQUALITIES
0
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
13CLIMATE ACTION
1
Research Products
14LIFE BELOW WATER
0
Research Products
15LIFE ON LAND
0
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
17PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents
27
Citations
81
h-index
6
This researcher does not have a WoS ID.
Scholarly Output
57
Articles
12
Views / Downloads
81578/22449
Supervised MSc Theses
23
Supervised PhD Theses
2
WoS Citation Count
37
Scopus Citation Count
70
Patents
0
Projects
4
WoS Citations per Publication
0.65
Scopus Citations per Publication
1.23
Open Access Source
45
Supervised Theses
25
| Journal | Count |
|---|---|
| Turkish Journal of Electrical Engineering and Computer Sciences | 3 |
| 11th European Conference on Information Warfare and Security 2012, ECIW 2012 | 2 |
| 13th Linguistic Annotation Workshop (LAW) -- Aug 01, 2019 -- Florence, Italy | 1 |
| 13. Ulusal Yazılım Mühendisliği Sempozyumu | 1 |
| 19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018 | 1 |
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57 results
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Now showing 1 - 10 of 57
Conference Object Citation - Scopus: 6Gender Prediction From Tweets With Convolutional Neural Networks: Notebook for Pan at Clef 2018(CEUR Workshop Proceedings, 2018) Sezerer, Erhan; Polatbilek, Ozan; Sevgili, Özge; Tekir, SelmaThis paper presents a system1 developed for the author profiling task of PAN at CLEF 2018. The system utilizes style-based features to predict the gender information from the given tweets of each user. These features are automatically extracted by Convolutional Neural Networks (CNN). The system mainly depends on the idea that the informativeness of each tweet is not the same in terms of the gender of a user. Thus, the attention mechanism is included to the CNN outputs in order to discriminate the tweets carrying more information. Our architecture was able to obtain competitive results on three languages provided by the PAN 2018 author profiling challenge with an average accuracy of 75.1% on local runs and 70.23% on the submission run.Conference Object Citation - Scopus: 12N-Hance at Semeval-2017 Task 7: a Computational Approach Using Word Association for Puns(Association for Computational Linguistics (ACL), 2017) Sevgili,Ö.; Ghotbi,N.; Tekir,S.This paper presents a system developed for SemEval-2017 Task 7, Detection and Interpretation of English Puns consisting of three subtasks; pun detection, pun location, and pun interpretation, respectively. The system stands on recognizing a distinctive word which has a high association with the pun in the given sentence. The intended humorous meaning of pun is identified through the use of this word. Our official results confirm the potential of this approach. © 2017 Association for Computational LinguisticsConference Object Citation - Scopus: 1Türkçe Tweetler Üzerinden Yapay Sinir Ağları ile Cinsiyet Tahminlemesi(Institute of Electrical and Electronics Engineers Inc., 2019) Sezerer, Erhan; Polatbilek, Ozan; Tekir, SelmaYazar ayrımlaması, yazarı bilinmeyen bir metin üzerinden yazarına dair cinsiyet, yaş ve dil gibi bazı anahtar özniteliklerin belirlenmesidir. Özellikle güvenlik ve pazarlama alanında önem arz etmektedir. Bu çalışmada, kullanıcıların tweetleri kullanılarak cinsiyetleri tahminlenmektedir. Yinelemeli Sinir Ağı (YSA) ve ilgi mekanizmasının birleşiminden oluşan bir model önerilmiştir. Bildiğimiz kadarıyla bu çalışma Twitter veri kümesi ile Türkçe’de ilk defa yapılmıştır. Önerilen model Türkçe, İngilizce, İspanyolca ve Arapça dillerinde sınanmış ve sırasıyla 80.63, 81.73, 78.22, 78.5 doğruluk değerlerine ulaşılmıştır. Elde edilen doğruluk değerleri Türkçe’de en gelişkin, diğer dillerde ise rekabetçi bir başarım ortaya koymaktadır.Article Asking the Right Questions To Solve Algebraic Word Problems(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2022) Çelik, Ege Yiğit; Orulluoğlu, Zeynel; Mertoğlu, Rıdvan; Tekir, SelmaWord algebra problems are among challenging AI tasks as they combine natural language understanding with a formal equation system. Traditional approaches to the problem work with equation templates and frame the task as a template selection and number assignment to the selected template. The recent deep learning-based solutions exploit contextual language models like BERT and encode the natural language text to decode the corresponding equation system. The proposed approach is similar to the template-based methods as it works with a template and fills in the number slots. Nevertheless, it has contextual understanding because it adopts a question generation and answering pipeline to create tuples of numbers, to finally perform the number assignment task by custom sets of rules. The inspiring idea is that by asking the right questions and answering them using a state-of-the-art language model-based system, one can learn the correct values for the number slots in an equation system. The empirical results show that the proposed approach outperforms the other methods significantly on the word algebra benchmark dataset alg514 and performs the second best on the AI2 corpus for arithmetic word problems. It also has superior performance on the challenging SVAMP dataset. Though it is a rule-based system, simple rule sets and relatively slight differences between rules for different templates indicate that it is highly probable to develop a system that can learn the patterns for the collection of all possible templates, and produce the correct equations for an example instance.Conference Object Citation - WoS: 10A Turkish Dataset for Gender Identification of Twitter Users(Assoc Computational Linguistics-ACL, 2019) Sezerer, Erhan; Polatbilek, Ozan; Tekir, SelmaAuthor profiling is the identification of an author's gender, age, and language from his/her texts. With the increasing trend of using Twitter as a means to express thought, profiling the gender of an author from his/her tweets has become a challenge. Although several datasets in different languages have been released on this problem, there is still a need for multilingualism. In this work, we propose a dataset of tweets of Turkish Twitter users which are labeled with their gender information. The dataset has 3368 users in the training set and 1924 users in the test set where each user has 100 tweets. The dataset is publicly available(1).Conference Object Doğal Dil Çıkarımı Modellerinde Bert Vektörlerinin Başarım Değerlendirmesi(Institute of Electrical and Electronics Engineers Inc., 2021) Oğul, İskender Ülgen; Tekir, SelmaDoğal dil çıkarımı, düşünce ifade eden cümlelerin arasındaki ilişkiyi; karşıtlık, gerekseme veya tarafsızlık olarak sınıflandırmayı hedefler. Sınıflandırma görevini gerçekleştirmek için metinsel kaynaklar, vektör ya da gömme olarak adlandırılan matematiksel gösterimlere dönüştürülür. Bu çalışmada, hem statik (Glove, OntoNotes5) hem de bağlamsal (BERT) kelime gömme yöntemleri kullanılmıştır. Fikirsel cümleler arasındaki mantıksal ilişkilerin sınıflandırılması zordur zira cümleler karmaşık gramer yapılarına sahiptir ve cümlelerin işlenerek mantıksal gösterimlere dönüştürülmesi geleneksel doğal dil işleme çözümleri ile yetersiz kalmaktadır. Bu çalışma, sınıflandırma görevini gerçekleştirmek için ayrıştırılabilir ilgi ve doğal dil çıkarımı için gelişmiş LSTM (ESIM) derin öğrenme modellerini kullanmıştır. En iyi sonuç olan %88 doğruluk değeri SNLI veri kümesi üzerinde ESIM-BERT ile elde edilmiştir.Conference Object Citation - WoS: 2Citation - Scopus: 4Çok-etiketli Film Türü Sınıflandırması için Türkçe Konu Modellemesi Veri Kümesi(Institute of Electrical and Electronics Engineers, 2020) Jabrayilzade, Elgün; Poyraz Arslan, Algın; Para, Hasan; Polatbilek, Ozan; Sezerer, Erhan; Tekir, SelmaStatistical topic modeling aims to assign topics to documents in an unsupervised way. Latent Dirichlet Allocation (LDA) is the standard model for topic modeling. It shows good performance on document collections, documents being relatively long texts but it has poor performance on short texts. Topic modeling on short texts is on the rise due to the potential of social media. Thus, approaches that are able to nd topics on short texts as well as long texts are sought. However, there is a lack of datasets that include both long and short texts which have the same ground-truth categories. In this work, we release a Turkish movie dataset which contain both short lm descriptions and long subscripts where lm genre can be considered as topic. Furthermore, we provide multi-label movie genre classication results using a Feed Forward Neural Network (FFNN) taking LDA document-topic or Doc2Vec dense representations. © 2020 IEEE.Conference Object Citation - WoS: 3Citation - Scopus: 4Recent Cyberwar Spectrum and Its Analysis(Curran Associates, 2012) Aslanoğlu, Rabia; Tekir, SelmaWar is an organized, armed, and often prolonged conflict that is carried on between states, nations or other parties. Every war instance includes some basic components like rising conditions, battlespace, weapons, strategy, tactics, and consequences. Recent developments in the information and communication technologies have brought about changes on the nature of war. As a consequence of this change, cyberwar became the new form of war. In this new form, the new battlespace is cyber space and the contemporary weapons are constantly being renovated viruses, worms, trojans, denial-of-service, botnets, and advanced persistent threat. In this work, we present recent cyberwar spectrum along with its analysis. The spectrum is composed of the Estonia Attack, Georgia Attack, Operation Aurora, and Stuxnet Worm cases. The methodology for analysis is to identify reasons, timeline, effects, responses, and evaluation of each individual case. Moreover, we try to enumerate the fundamental war components for each incident. The analysis results put evidences to the evolution of the weapons into some new forms such as advanced persistent threat. Another outcome of the analysis is that when approaching to the end, confidentiality and integrity attributes of information are being compromised in addition to the availability. Another important observation is that in the last two cases, the responsive actions were not possible due to the lack of the identities of the offending parties. Thus, attribution appears as a significant concern for the modern warfare. The current sophistication level of the cyber weapons poses critical threats to society. Particularly developed countries that have high dependence on information and communication technologies are potential targets since the safety of the critical infrastructures like; healthcare, oil and gas production, water supply, transportation and telecommunication count on the safety of the computer networks. Being aware of this fact, every nation should attach high priorities to cyber security in his agenda and thus behave proactively.Article Gender Bias in Occupation Classification From the New York Times Obituaries(Dokuz Eylül Üniversitesi, 2022) Atik, Ceren; Tekir, SelmaTechnological developments such as artificial intelligence can strengthen social prejudices prevailing in society, regardless of the developer's intention. Therefore, researchers should be aware of the ethical issues that may arise from a developed product/solution. In this study, we investigate the effect of gender bias on occupational classification. For this purpose, a new dataset was created by collecting obituaries from the New York Times website and is provided in two different versions: With and without gender indicators. Category distributions from this dataset show that gender and occupation variables have dependence. Thus, gender affects occupation classification. To test the effect, we perform occupation classification using SVM (Support Vector Machine), HAN (Hierarchical Attention Network), and DistilBERT-based classifiers. Moreover, to get further insights into the relationship of gender and occupation in classification problems, a multi-tasking model in which occupation and gender are learned together is evaluated. Experimental results reveal that there is a gender bias in job classification.Article Citation - WoS: 2Citation - Scopus: 2Incorporating Concreteness in Multi-Modal Language Models With Curriculum Learning(MDPI, 2021) Sezerer, Erhan; Tekir, SelmaOver the last few years, there has been an increase in the studies that consider experiential (visual) information by building multi-modal language models and representations. It is shown by several studies that language acquisition in humans starts with learning concrete concepts through images and then continues with learning abstract ideas through the text. In this work, the curriculum learning method is used to teach the model concrete/abstract concepts through images and their corresponding captions to accomplish multi-modal language modeling/representation. We use the BERT and Resnet-152 models on each modality and combine them using attentive pooling to perform pre-training on the newly constructed dataset, which is collected from the Wikimedia Commons based on concrete/abstract words. To show the performance of the proposed model, downstream tasks and ablation studies are performed. The contribution of this work is two-fold: A new dataset is constructed from Wikimedia Commons based on concrete/abstract words, and a new multi-modal pre-training approach based on curriculum learning is proposed. The results show that the proposed multi-modal pre-training approach contributes to the success of the model.