Detecting Defective Expressions in Turkish Sentences Using a Hybrid Deep Learning Method

Defective expression is a grammatical term that refers to both semantic and morphologic ambiguities in Turkish sentences. In earlier studies, Natural Language Processing (NLP) techniques have been used by constructing rule-based language-specific models. However, despite less demanding annotations requirements and ease of incorporating external knowledge, rule-based systems have some significant obstacles in terms of processing efficiency. Deep learning techniques such as long short-term memory (LSTM) or convolutional neural network (CNN) have made significant advances in recent years, which led to an unprecedented boost in NLP applications in terms of performance. In this study, a hybrid approach of LSTM and CNN (C-LSTM) for detecting defective expressions in addition to traditional machine learning classifiers such as support vector machine (SVM) and random forest (RF) to compare the results in terms of accuracy are proposed. The proposed hybrid approach achieved higher accuracy than the existing deep neural models of CNN and LSTM, in addition to the traditional classifiers of SVM and random forest. This study shows that deep neural approaches come into prominence for text classification compared to traditional classifiers.</p

A Novel Approach for Detecting Defective Expressions in Turkish

The use of machine learning has been increasing rapidly in recent years by being more efficient in comparison to rule-based techniques. However, NLP (Natural Language Processing) operations generally require language specific solutions, especially semantic problems. Therefore, deep learning techniques are the best approach for detecting ambiguities in Turkish sentences as they do not need rule-based code implementations. Embedding word vectors are the vectorial visualizations of texts and are beneficial to analyze the word relationships in terms of semantics. In this study, CNN (Convolutional Neural Network) model is proposed to detect defective semantic expressions in Turkish sentences, and the accuracy results of the model are decided to be analyzed. This study makes a crucial contribution for Turkish in terms of semantic analysis and for further related performances.</p

Connotation: Educational Mobile Game Application For Turkish

Nowadays, rapidly developing technologies and word games have great effect on students about learning word meanings correctly and abilities to speak properly. In this work, implementation of game software, which can run on mobile devices and in which students may learn meanings of Turkish words with fun, is explained and results are discussed. “Educational computer games” is an important concept in Turkey, and it has not been used in teaching curriculum, yet. In order to resolve this deficiency, a list was formed by keywords, meanings, connotations and images from topics of secondary school curriculum books. Game data is analysed by using statistical methods and reports are generated for parents, teachers and administrators to help watching personal development of students. Moreover, “Turkish Connotation List (Dictionary)”, the collection of connotation words, is generated to support developing Turkish Semantic Network, which has very important place in development of Turkish language.</p