Confidence Indexing of Automated Detected Synsets: A Case Study on Contemporary Turkish Dictionary

In this study, a novel confidence indexing algorithm is proposed to minimize human labor in controlling the reliability of automatically extracted synsets from a non-machine-readable monolingual dictionary. Contemporary Turkish Dictionary of Turkish Language Association is used as the monolingual dictionary data. First, the synonym relations are extracted by traditional text processing methods from dictionary definitions and a graph is prepared in Lemma-Sense network architecture. After each synonym relation is labeled by a proper confidence index, synonym pairs with desired confidence indexes are analyzed to detect synsets with a spanning tree-based method. This approach can label synsets with one of three cumulative confidence levels (CL-1, CL-2, and CL-3). According to the confidence levels, synsets are compared with KeNet which is the only open access Turkish Wordnet. Consequently, while most matches with the synsets of KeNet is determined in CL-1 and CL-2 confidence levels, the synsets determined at CL-3 level reveal errors in the dictionary definitions. This novel approach does not find only the reliability of automatically detected synsets, but it can also point out errors of detected synsets from the dictionary.

Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches

Conducting NLP for Turkish is a lot harder than other Latin-based languages such as English. In this study, by using text mining techniques, a pre-processing frame is conducted in which TF-IDF values are calculated in accordance with a linguistic approach on 7,731 tweets shared by 13 famous economists in Turkey, retrieved from Twitter. Then, the classification results are compared with four common machine learning methods (SVM, Naive Bayes, LR, and integration LR with SVM). The features represented by the TF-IDF are experimented in different N-grams. The findings show the success of a text classification problem is relative with the feature representation methods, and the performance superiority of SVM is better compared to other ML methods with unigram feature representation. The best results are obtained via the integration method of SVM with LR with the Acc of 82.9%. These results show that these methodologies are satisfying for the Turkish language.

English-Turkish Literary Translation Through Human-Machine Interaction

This article investigates perceptions of technology-mediated translations of literary texts by two groups: translation students and professional literary translators. The participants post-edited an excerpt from a classic Dickens novel into Turkish using a machine translation (MT) system of their choice. The analysis of the post-edited texts, participants’ answers to survey questions, and interviews with professional translators suggest that MT is currently a long way from being an essential part of any literary translation practice for the English–Turkish language pair. Translators’ interactions with MT and negative attitudes toward it may change in a positive direction as MT improves and translation practice evolves.

Examination of Turkish Language Teachers’ Self-Efficacy Perceptions of Teaching Thinking Skills in Terms of Various Variables

The aim of this study was to examine Turkish Language teachers’ self-efficacy perceptions of teaching thinking skills in terms of the variables of age, professional experience, educational status, and taking course on thinking skills. The screening model was used in the study. The current pandemic process was taken into account and the convenience sampling method was used while determining the study group. Turkish Language teachers forming the study group consisted of 109 females and 68 males. “Teachers’ Self-efficacy towards teaching thinking scale” was used as the data collection tool. The data of the study were analyzed using the statistical package program. In conclusion, no significant difference was found between the Turkish Language teachers’ self-efficacy perceptions of teaching thinking skills and gender. It was concluded that Turkish Language teachers’ self-efficacy perceptions of teaching thinking skills differed according to professional experience. It was concluded that there was no significant difference between Turkish Language teachers’ self-efficacy perceptions of teaching thinking skills and their educational status. It was concluded that there was a significant difference between Turkish Language teachers’ self-efficacy perceptions of teaching thinking skills and taking course on thinking skills.

Phanariot Tongues: The Mavrocordatos Family and the Power of the Turkish Language in the Eighteenth-Century Ottoman Empire

The Phanariots — Grecophone Christian elites who ruled the Danubian principalities in the eighteenth century — were the only non-Muslims in the Ottoman Empire who claimed power by virtue of their command of the Turkish language. Why were they the rare exception and what does their story reveal about the ways in which power and language were intertwined in the early modern Ottoman Empire? The implicit power relations embedded in the Turkish language are rendered visible in a unique text written in 1731 in which Constantine Mavrocordatos, a Phanariot prince, attempted to school his younger brother in Turkish through a series of twelve, play-like dialogues. The dialogues did not aim to teach the formal grammar of Turkish but to demonstrate the power of speech by familiarizing the reader with the eloquent and witty repartee of Ottoman bureaucrats. Through an analysis of the text — which includes reestablishing its authorship and date of composition — the article examines the Phanariots’ liminal position in Ottoman governance, especially in the newly ascendant imperial bureaucracy, through the prism of language. In doing so, it also rewrites the place of the Mavrocordatos family in the story of the Enlightenment in the Ottoman Empire.

Words of Turkish Origin in the Works of J. R. R. Tolkien

J. R. R. Tolkien is undoubtedly one of the most widely read epic fiction writers, translated into almost forty world languages. His works describe the entire history of an imaginary world, from the very beginning of its creation until the creation of man and are imbued with a constant struggle between good and evil. On the opposite sides, there are different races of humanoid creatures, among which are: elves, dwarves, orcs, goblins, trolls, etc. They all have elaborate genealogies and cultural characteristics. The extremely rich philological education of the author himself contributed to that. The connections between Tolkien’s work and Nordic myths have been shown in detail in science so far. This is most obvious when choosing mythological symbols and names. The author even created an elven language inspired by the Finnish language, for which he used runic alphabet. However, the names of the places where orcs, goblins and other servants of evil live, as well as their personal names, were not created after the example of elves. According to their phonetic characteristics, these names are significantly different from elven and human ones. In this paper, attention will be focused on such names, considering that they possess phonetic and semantic characteristics of the Turkish language, especially its older variants, and that they carry certain meanings that still exist in the modern Turkish language.

Problems of Translation of Parenthetic Clauses from Russian into Turkish in Social and Political Texts

Actively developing mass media field generates high demand for specialists who are able to translate texts of social and political topics from Russian into a foreign language and vice versa quickly and efficiently. While learning, students make common mistakes, which should be carefully corrected and prevented. The purpose of this article is to identify the most common mistakes made in translations of parenthetic clauses from Turkish into Russian and to develop recommendations for translating this type of sentences. The research is based on the translations of parenthetic clauses from Russian into Turkish made by three Turkish native speakers who are proficient in Russian, and ten Russian-speaking informants who are 4th-year undergraduate and graduate students of the leading Russian universities where Turkish language is taught as a major. The lack of research papers on the topic indicates the scientific novelty of the study. As a result of the study, the authors conclude that translation of parenthetic clauses presents significant difficulties for students and propose the following recommendations: there are two variants of translating Russian parenthetic clauses into Turkish - a subordinate clause with the conjunction ki and a participle clause with a participle of present-past tenses -(y)An . The first variant reflects the meaning expressed by Russian parenthetic clause most accurately, i.e. makes an emphasis. When referring to this variant, it is necessary to pay attention to the fact that the subordinate clause must have its own subject, which is most often presented by the pronoun bu this, or its subject must coincide with the subject of the main clause. The second variant mainly performs a determinative function, therefore, the sentence emphasis is made lexically. The stylistics should also be considered: a subordinate clause with the conjunction ki , as emotionally more powerful, is mainly used in analytical newspaper publications, while a participle clause with the participle -(y)An is more neutral stylistically. Therefore, if there are any difficulties with choosing the correct way to translate parenthetic clauses from Russian into Turkish, it is recommended to refer to - the (y)An participle.