Yazar "Velioglu, Riza" seçeneğine göre listele

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    A comparative study of neural machine translation models for Turkish language
    (Ios Press, 2022) Ozdemir, Ozgur; Akin, Emre Salih; Velioglu, Riza; Dalyan, Tugba
    Machine translation (MT) is an important challenge in the fields of Computational Linguistics. In this study, we conducted neural machine translation (NMT) experiments on two different architectures. First, Sequence to Sequence (Seq2Seq) architecture along with a variation that utilizes attention mechanism is performed on translation task. Second, an architecture that is fully based on the self-attention mechanism, namely Transformer, is employed to perform a comprehensive comparison. Besides, the contribution of employing Byte Pair Encoding (BPE) and Gumbel Softmax distributions are examined for both architectures. The experiments are conducted on two different datasets: TED Talks that is one of the popular benchmark datasets for NMT especially among morphologically rich languages like Turkish and WMT18 News dataset that is provided by The Third Conference on Machine Translation (WMT) for shared tasks on various aspects of machine translation. The evaluation of Turkish-to-English translations' results demonstrate that the Transformer model with combination of BPE and Gumbel Softmax achieved 22.4 BLEU score on TED Talks and 38.7 BLUE score on WMT18 News dataset. The empirical results support that using Gumbel Softmax distribution improves the quality of translations for both architectures.
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    Sentiment Analysis Using Learning Approaches over Emojis for Turkish Tweets
    (IEEE, 2018) Velioglu, Riza; Yildiz, Tugba; Yildirim, Savas
    With the rise of the usage and interest on social media platforms, emojis have become an increasingly important part of the written language and one of the most important signals for micro-blog sentiment analysis. In this paper, we employed and evaluated classification models using two different representations based on hag-of-words and fasilText to address the problem of sentiment analysis over emojis/emoticons for Turkish positive, negative and neutral tweets. At first, the hag of-words approach is used as a simple and efficient baseline method for tweet representation, where the classifiers such as Naive Bayes, Logistic Regression, Support Vector Machines, Decision Trees have been applied to these tweets. Secondly, we utilized fastText to represent tweets as word n-grams for sentiment analysis problem. The results show that there is no significant difference between the two models. While fastText shows 79% and the Linear Regression classifier obtains 77% Fl-score for binary classification, fastText performs 62% and Linear Regression has 58% Fl-score for multi-class classification. This study is considered as the first study that contributes to the literature by applying different vector representations such as bag-of-words and fastText to predict Turkish tweets over emojis. This study can also be utilized to predict emojis on social media context in the future.