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Metin Sınıflandırma Doğal Dil İşleme (DDİ) alanında önemli bir yere sahiptir. Son zamanlarda metinsel verilerin artması ve otomatik etiketlenmesi gerekliliği, metin sınıflandırma probleminin önemini artırmıştır. Geleneksel yaklaşımlardan öne çıkan kelime torbası yöntemi yıllardır metin sınıflandırmasında başarılı olmaktadır. Son zamanlarda sinir ağları dil modelleri DDİ problemlerine başarılı bir şekilde uygulanmış ve bazı alanlarda büyük başarı kaydetmişlerdir. Yapay Sinir Ağları (YSA) temelli mimarilerin en önemli avantajı daha etkili kelime ve metin gösterilimlerin oluşturmasıdır. Bu gösterilimler, geleneksel yöntemlere göre daha az boyutlu ve daha etkili bulunmuştur. Özellikle anlambilimsel ve sözdizimsel analizlerde başarılı uygulamalar yapılmıştır. Öte yandan daha uzun vektörlerle gösterilim kullanan geleneksel kelime torbası yöntemleri, metin gösterilimleri anlamında hala gücünü korumaktadır. Ancak Türkçe için bu iki yaklaşımın herhangi bir karşılaştırılması yapılmamıştır. Bu çalışmada, geleneksel kelime torbası yaklaşımı ile sinir ağı temelli yeni gösterilim yaklaşımları metin sınıflandırması açısından karşılaştırılmıştır. Bu çalışmalarda gördük ki etkili özellik seçimleri geleneksel yöntemlerinin hala yeni kuşak kelime gömme (word embeddings) yaklaşımı ile yarışacak düzeydedir. Son olarak deneylerimizi bu iki yaklaşım açısından çeşitlendirerek raporladık ve Türkçe için başarılı metin sınıflandırma mimarisini bu raporda ayrıntılı tartıştık. Text categorization plays important role in the field of Natural Language Processing. Recently, the rapid growth in the amount of textual data and requirement of automatic annotation makes the problem of text categorization more important. As a prominent one of the traditional methods, the bag-of-words approach has been successfully applied to text categorization problem for years. Recently, Neural Network Language Models (NNLM) have achieved successful results for various problems of Natural Language Processing (NLP). The most important advantage of the NNLM is to provide effective word and document representations. Those representations are lower dimensional and are found to be more effective than traditional methods. They have been exploited successfully for semantic and syntactic analysis. On the other hand, the traditional bag-of-words approaches that use one-hot long vector representation are still considered powerful in terms of their accuracy in document classification. However, comparing these approaches for Turkish language has not been attempted before. In this study, we compared them within a variety of analysis. We observed that the traditional bagof-word representation utilizing an effective feature selection and a machine learning algorithm aligned with it have comparable performance with new generation vector based methods, namely word embeddings. In this study, we have conducted various experiments comparing these approaches and designated an effective text categorization architecture for Turkish Language.

Recently, Neural Network Language Models have been effectively applied to many types of Natural Language Processing (NLP) tasks. One popular type of tasks is the discovery of semantic and syntactic regularities that support the researchers in building a lexicon. Word embedding representations are notably good at discovering such linguistic regularities. We argue that two supervised learning approaches based on word embeddings can be successfully applied to the hypernym problem, namely, utilizing embedding offsets between word pairs and learning semantic projection to link the words. The offset-based model classifies offsets as hypernym or not. The semantic projection approach trains a semantic transformation matrix that ideally maps a hyponym to its hypernym. A semantic projection model can learn a projection matrix provided that there is a sufficient number of training word pairs. However, we argue that such models tend to learn is-a-particular-hypernym relation rather than to generalize is-a relation. The embeddings are trained by applying both the Continuous Bag-of Words and the Skip-Gram training models using a huge corpus in Turkish text. The main contribution of the study is the development of a novel and efficient architecture that is well-suited to applying word embeddings approaches to the Turkish language domain. We report that both the projection and the offset classification models give promising and novel results for the Turkish Language.

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Purpose Change is continuous and leaves many digital traces in contemporary organizations, while research on change usually lacks such continuity. The purpose of this paper is to test and explore the claim that change can be monitored through employee discourse. In doing so, the authors introduce basic text mining methods to detect prevailing keywords and their changes over time. Such monitoring of content and its change promises a continuous feedback and improvement for change management efforts. Design/methodology/approach The authors use a mixed research design, combining an ethnographic approach with digital methods. The quantitative element of the method involves applying text mining techniques to a document corpus that is representative of people in organizations, and is originally collected as part of a relatively common performance management system. The findings about discursive categories and their change patterns through time are then combined with observations and secondary information about change management for interpretation. Findings By combining these measurements with additional information about the change program in focus, the authors develop an interpretation of the dynamics of organizational change. Results showed that even in a successfully implied change effort that realize the planned targets, change does not occur directly and fully, with some elements of discourse being more persistent than others. Research limitations/implications Method of the research presents a new way of monitoring discursive change. Its incorporation into practice potentially allows for timely correction of change efforts and increasing possibility of success. Originality/value This research provides a framework for understanding how, and to what extent, planned change efforts effect organizations. Furthermore, the method developed in this research presents an innovative approach to monitor discursive change and timely managerial intervention.