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A Student Grammar of Turkish
A Student Grammar of Turkish is a concise introduction to Turkish grammar, designed specifically for English-speaking students and professionals. Written with the needs of the learner very much in mind, it sets out the grammar of the language in a clear and jargon-free style. The book not only explains the fundamentals of the grammar, but also tests students' understanding in an interactive way with more than 200 exercises. Key grammar points are summarised in tables and there are numerous illustrative examples. A list of grammatical terms used in the book and a key to all the exercises are also provided. This essential grammar and exercise book can be used as a supplement for students studying the language, with a dual function as a reference guide to look up grammar points, and as a resource from which exercises can be set and language skills practised.
eBook
Study of automatic text summarization approaches in different languages
Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic text summarization systems are developed. Text summarization is a process of extracting useful information from large documents and compressing them into short summary preserving all important content. This survey paper hand out a broad overview on the work done in the field of automatic text summarization in different languages using various text summarization approaches. The focal centre of this survey paper is to present the research done on text summarization on Indian languages such as, Hindi, Punjabi, Bengali, Malayalam, Kannada, Tamil, Marathi, Assamese, Konkani, Nepali, Odia, Sanskrit, Sindhi, Telugu and Gujarati and foreign languages such as Arabic, Chinese, Greek, Persian, Turkish, Spanish, Czeh, Rome, Urdu, Indonesia Bhasha and many more. This paper provides the knowledge and useful support to the beginner scientists in this research area by giving a concise view on various feature extraction methods and classification techniques required for different types of text summarization approaches applied on both Indian and non-Indian languages.
Journal Article
The effect of the conceptual metaphor theory on the teaching of orientation idioms in teaching Turkish as a foreign language
The Challenge Learners who attend a foreign language course aim to learn the lexis and grammar of that language and develop their basic language skills. Does this mean that idioms which are among the essential elements of any language are taught at a satisfactory level to learners? Can learners be taught a strategy that would enable them to guess the meanings of unknown idioms in the contexts they are used? This article presents the results of a study that developed activities to teach idioms that contained orientation metaphors and incorporated them into a language program. In this study, the effects of the conceptual metaphor theory (CMT) on the teaching of orientation idioms was investigated. The study was designed as an action research project and was conducted with 45 B2 level students from 21 countries who were native speakers of 10 different languages. The students were learning Turkish to pursue the undergraduate degrees in Turkish universities. The students were learning and using Turkish for their daily interactions with Turkish people but continued to use their native languages in their closely knit communities. Thus, they formed good examples of true bilingual individuals. At the planning stage of the study, the orientation idioms in the Turkish as a foreign language course books were identified to be used in the teaching activities during the study and a list of orientation idioms was prepared. The students were asked to write the meanings of those idioms and use each one in a sentence to determine whether they had already learned those idioms. The orientation idioms in the B2 level course books were taught through activities based on CMT. At the end of the study, the students were asked to write the meanings of the idioms and use each one in a sentence once again. Pre‐ and poststudy mean scores of the students' performances were compared. It was determined that the implementation based on CMT developed the students' ability to learn metaphors and use them contextually appropriately. Furthermore, it was observed that the teaching activities based on the theory made it easier for the students to learn the idioms and increased their retention in the students' minds. It was also observed that the students had developed the skills of guessing the meanings of new orientation idioms from the contexts they were used in and using them more easily.
Journal Article
OffensEval 2023: Offensive language identification in the age of Large Language Models
The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the community with benchmark datasets in Arabic, Danish, English, Greek, and Turkish. The datasets were annotated using the OLID hierarchical taxonomy, which since then has become the de facto standard in general offensive language identification research and was widely used beyond OffensEval. We present a survey of OffensEval and related competitions, and we discuss the main lessons learned. We further evaluate the performance of Large Language Models (LLMs), which have recently revolutionalized the field of Natural Language Processing. We use zero-shot prompting with six popular LLMs and zero-shot learning with two task-specific fine-tuned BERT models, and we compare the results against those of the top-performing teams at the OffensEval competitions. Our results show that while some LMMs such as Flan-T5 achieve competitive performance, in general LLMs lag behind the best OffensEval systems.
Journal Article
The Development and Experimental Evaluation of a Multilingual Speech Corpus for Low-Resource Turkic Languages
The development of parallel audio corpora for Turkic languages, such as Kazakh, Uzbek, and Tatar, remains a significant challenge in the development of multilingual speech synthesis, recognition systems, and machine translation. These languages are low-resource in speech technologies, lacking sufficiently large audio datasets with aligned transcriptions that are crucial for modern recognition, synthesis, and understanding systems. This article presents the development and experimental evaluation of a speech corpus focused on Turkic languages, intended for use in speech synthesis and automatic translation tasks. The primary objective is to create parallel audio corpora using a cascade generation method, which combines artificial intelligence and text-to-speech (TTS) technologies to generate both audio and text, and to evaluate the quality and suitability of the generated data. To evaluate the quality of synthesized speech, metrics measuring naturalness, intonation, expressiveness, and linguistic adequacy were applied. As a result, a multimodal (Kazakh–Turkish, Kazakh–Tatar, Kazakh–Uzbek) corpus was created, combining high-quality natural Kazakh audio with transcription and translation, along with synthetic audio in Turkish, Tatar, and Uzbek. These corpora offer a unique resource for speech and text processing research, enabling the integration of ASR, MT, TTS, and speech-to-speech translation (STS).
Journal Article
A new methodology for automatic creation of concept maps of Turkish texts
Concept maps are two-dimensional visual tools that describe the relationships between concepts belonging to a particular subject. The manual creation of these maps entails problems such as requiring expertise in the relevant field, minimizing visual complexity, and integrating maps, especially in terms of text-intensive documents. In order to overcome these problems, automatic creation of concept maps is required. On the other hand, the production of a fully automated and human-hand quality concept map from a document has not yet been achieved satisfactorily. Motivated by this observation, this study aims to develop a new methodology for automatic creation of the concept maps from Turkish text documents for the first time in the literature. In this respect, within the scope of this study, a new heuristic algorithm has been developed using the Turkish Natural Language Processing software chain and the Graphviz tool to automatically extract concept maps from Turkish texts. The proposed algorithm works with the principle of obtaining concepts based on the dependencies of Turkish words in sentences. The algorithm also determines the sentences to be added to the concept map with a new sentence scoring mechanism. The developed algorithm has been applied on a total of 20 data sets in the fields of Turkish Literature, Geography, Science, and Computer Sciences. The effectiveness of the algorithm has been analyzed with three different performance evaluation criteria, namely precision, recall and F-score. The findings have revealed that the proposed algorithm is quite effective in Turkish texts containing concepts. It has also been observed that the sentence selection algorithm produces results close to the average value in terms of the performance criteria being evaluated. According to the findings, the concept maps automatically obtained by the proposed algorithm are quite similar to the concept maps extracted manually. On the other hand, there is a limitation of the developed algorithm since it is dependent on a natural language processing tool and therefore requires manual intervention in some cases.
Journal Article
Abstractive text summarization and new large-scale datasets for agglutinative languages Turkish and Hungarian
Due to the exponential growth in the number of documents on the Web, accessing the salient information relevant to a user need is gaining importance, which increases the popularity of text summarization. Recent progress in deep learning shifted the research in text summarization from extractive methods towards more abstractive approaches. The research and the available resources remain mostly limited to the English language, which prevents progress in other languages. There is need in low-resourced languages for gathering large-scale resources suitable for such tasks. In this study, we release two large-scale datasets (TR-News and HU-News) that can serve as benchmarks in the abstractive summarization task for Turkish and Hungarian. The datasets are primarily compiled for text summarization, but are also suitable for other tasks such as topic classification, title generation, and key phrase extraction. Morphology is important for these agglutinative languages since meaning is carried mostly within the morphemes of the words. We utilize these morphological properties for tokenization to retain the semantic information and reduce the vocabulary sparsity introduced by the agglutinative nature of these languages. Using the datasets compiled, we propose linguistically-oriented tokenization methods (SeperateSuffix and CombinedSuffix) and evaluate them on the state-of-the-art abstractive summarization models. The SeperateSuffix method achieves the highest ROUGE-1 score on the TR-News dataset and provides promising results on the HU-News dataset. In another experiment, we show that the multilingual cased BERT model outperforms monolingual BERT models for both languages and reaches the highest ROUGE-1 score on the HU-News dataset. Lastly, we provide qualitative analysis of the generated summaries on the TR-News dataset.
Journal Article
Turkish abstractive text summarization using pretrained sequence-to-sequence models
The tremendous amount of increase in the number of documents available on the Web has turned finding the relevant piece of information into a challenging, tedious, and time-consuming activity. Accordingly, automatic text summarization has become an important field of study by gaining significant attention from the researchers. Lately, with the advances in deep learning, neural abstractive text summarization with sequence-to-sequence (Seq2Seq) models has gained popularity. There have been many improvements in these models such as the use of pretrained language models (e.g., GPT, BERT, and XLM) and pretrained Seq2Seq models (e.g., BART and T5). These improvements have addressed certain shortcomings in neural summarization and have improved upon challenges such as saliency, fluency, and semantics which enable generating higher quality summaries. Unfortunately, these research attempts were mostly limited to the English language. Monolingual BERT models and multilingual pretrained Seq2Seq models have been released recently providing the opportunity to utilize such state-of-the-art models in low-resource languages such as Turkish. In this study, we make use of pretrained Seq2Seq models and obtain state-of-the-art results on the two large-scale Turkish datasets, TR-News and MLSum, for the text summarization task. Then, we utilize the title information in the datasets and establish hard baselines for the title generation task on both datasets. We show that the input to the models has a substantial amount of importance for the success of such tasks. Additionally, we provide extensive analysis of the models including cross-dataset evaluations, various text generation options, and the effect of preprocessing in ROUGE evaluations for Turkish. It is shown that the monolingual BERT models outperform the multilingual BERT models on all tasks across all the datasets. Lastly, qualitative evaluations of the generated summaries and titles of the models are provided.
Journal Article
A Comparative Analysis of Global English Textbooks in Terms of the Notion of English as an International Language
The global spread of English in recent decades has raised important questions about the long-standing dominance of nativespeakerism ideology in the field of English Language Teaching (ELT). Current research on ELT materials critically examines the prevalent approach that prioritizes the inner circle countries, their use of English, and their cultural values. These studies also emphasize a growing demand for a broader perspective that integrates both local and global diversity. However, this integration appears to be minimal and remains a relatively underexplored area in current ELT resources. The present study aims to address this gap, analyzing two global textbooks widely used for teaching English in Türkiye: Language Hub Elementary and English File Fourth Edition Elementary. The analysis was conducted within the notion of English as an International Language (EIL) based on Kachru's framework known as the Three Concentric Circles of the English Language. The findings of the analysis revealed that both textbooks fall short of representing the wide range of uses of English and its diverse users worldwide. Language Hub Elementary takes a more inclusive approach by including a variety of characters, accents, and cultural elements from the expanding circle countries. However, English File Fourth Edition Elementary shows clear bias towards inner circle characters, their use of standard language, and elements of Anglo-Saxon culture. The rich tapestry of local cultures in the countries of the outer circle is absent from both books. Similarly, Language Hub Elementary lacks any Turkish cultural elements, while English File Fourth Edition Elementary only offers limited coverage. The findings of this study emphasize the need for more inclusive and culturally diverse ELT materials in a global context and have important implications for both material designers and ELT scholars.
Journal Article