Eserlere Göre Listeleme

Distributed representations (DR) are used to capture semantic and syntactic patterns in language by analyzing the distributional relationships of words within textual data. The modeling methods that produce DR are based on the assumption (distributional hypothesis) that "words that occur in the same context tend to have similar meanings," which is inherent to the nature of language. These modeling methods, due to their unsupervised nature, can be trained without human judgment input, allowing researchers to train large datasets at relatively low costs. Although word-based models perform effectively for languages with limited vocabularies, such as English, they exhibit considerable inefficiency when applied to morphologically rich languages with unlimited vocabularies, such as Turkish. We observed that n-gram and statistical segmentation methods, which are commonly used in subword modeling to address the issues of out-of-vocabulary and rare-words, are highly sensitive to orthographic similarity. Consequently, these methods struggle to distinguish between unrelated concepts (e.g., shrink - shrine). Moreover, we noted that the impact of morphological segmentation methods on these types of problems has shown inconsistent results in the literature. This thesis aims to make conceptual assumptions and improvements concerning different types of semantic relationships (e.g., relatedness and similarity), to model the role of language morphology as an input in subword DR models, and to develop the dataset generation methodologies and evaluation methods to measure this effect. Within the scope of the study, different models and segmentation methods were empirically tested, the AnlamVer and OSimUnr datasets were produced, and the task of relatedness classification and associated evaluation methods were proposed to measure the noise introduced by segmentation to the model. Our experiments demonstrate that morphological segmentation produces significantly less noise compared to n-gram-based methods and can lead to substantial performance improvements depending on the nature of the task.

“Sait Faik, Burgaz çalılıklarından çekti bir kızılcık dalı kopardı, kalem gibi yonttu, ucunu yaşama batırdı ve yazmaya koyuldu. Türk hikâyeciliği Ömer Seyfettin’den sonra Memduh Şevket Esendal, Fahri Celalettin gibi ustaların sürdürdüğü bir türdü. Sabahattin Ali, Refik Halit’in memleket hikâyeciliğine diyalektik bir görüş katmış ve bu yeniliği ile 1940’ların tek ismi olmuştu . Sait Faik ise onların yapmadığı bir şeyi yaptı. Bir konuyu deği l, yaşamın bir parçasını işliyordu. Bir tez savunmuyor, bir yaşantıyı yansıtıyordu. İnsan sevgisi dolu, doğa sevgisi dolu bir yüreği vardı. Neye baksa bu sevgi ile ısınıyor, ışıklanıyordu. Biz anc ak o el attıktan sonradır ki, en önemsiz görünen insanların ve şeylerin zevkine eriştik.”