TR-Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1927
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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: 1Understanding the Psychological and Financial Correlates for Consumer Credit Use;(Sosyoekonomi Society, 2024) Ertuğrul, Seyit; Sayar, Alperen; Şahin, Türkay; Çakar,Tuna; Ertuğru, SeyitThis study investigated the behavioural and cognitive predictors of consumer credit usage to develop a behavioural credit risk assessment procedure for a factoring company. Participants completed surveys measuring personality traits, self-esteem, material and monetary values, compulsive and impulsive buying tendencies, self-control, and impulsiveness. Financial surveys also assessed financial literacy and knowledge of financial concepts. The results indicated that extraversion, conscientiousness, emotional stability, and experiential self-control were significant predictors of consumer credit usage. These findings suggest that a finance company can use these personality traits and financial characteristics to develop a more accurate and effective credit risk assessment procedure, such as psychometric tests. © 2024, Sosyoekonomi Society. All rights reserved.Article Detecting the Effect of Voice-Over in Tv Ads Via Optic Brain Imaging (fnirs) and In-Depth Interview Methods(Istanbul Univ, 2016) Çakar, Tuna; Girisken, YenerVoice-overs are used extensively to increase the effectiveness of the TV ads especially in the last decade. The main purpose is to provide the brand message via a clear feature that will inevitably grab the attention of the viewers. The current study contains the neuro tests of 12 TV ads in banking and finance sectors on 168 participants in 8 groups. Optic brain imaging (fNIRS) and in-depth interviews were the methodologies utilized during the test of these TV ads. The obtained results indicate that the use of voice-over during the TV ads possibly causes the decrease in attention and emotional engagement levels of the participants.