Word embeddings are successfully employed in various Natural Language Processing
tasks, but training them requires large amount of text, which is scarce for Turkish. In this …

The use of word embedding models and deep learning algorithms are currently the most
common and popular trends to enhance the overall performance of a text classification …

As a well-known Natural Language Processing (NLP) task, text classification can be defined
as the process of categorizing documents depending on their content. In this process …

Traditional manual text classification method has been unable to cope with the current huge
amount of data volume. The improvement of deep learning technology also accelerates the …

The text classification task has a wide range of application domains for distinct purposes,
such as the classification of articles, social media posts, and sentiments. As a natural …

Today, extreme amounts of data are produced, and this is commonly referred to as Big Data.
A significant amount of big data is composed of textual data, and as such, text processing …

Due to the large amounts of words usually present in documents, some of their appearances
can complicate the classification process and make it less accurate. Accordingly, word …

Document classification has played a major role in many fields like information retrieval,
data mining, etc. where machine learning and deep learning models can be applied. But …

Abstract Recently, Word Embeddings have been introduced as a major breakthrough in
Natural Language Processing (NLP) to learn viable representation of linguistic items based …

Natural language processing is the current topic due to many important tasks like document
classification, named entity recognition, opinion mining, sentiment analysis, textual …