… Traditional text classifiers often rely on many human-designed … volutional neural network
for text classification without human-… Because the word representation in our model is a word …

… Text classification is a foundational task in many natural language processing applications.
All traditional text … (like word2vec) to directly generate word vectors at the first step. However, …

… different techniques to build models for text classification. The first two techniques which
are … in text classification and we have tried to improve the accuracy by introducing next word …

… number of word embedding studies is still limited. The primary focus of the current study is
text classification performed for the Turkish language using pre-trained word embedding and …

… To evaluate the goodness of features selected by our method, we use the features in text
classification task. Our features are compared with the features selected by other 3 methods. …

… text documents in a set of predefined classes, using different supervised algorithms. Objectives:
We focus on the text classification … In the second approach, the text classification treats …

… two feature evaluation metrics for the … word in a document is independent of the word’s
position and the occurrence of other words in the document. Denote the number of times word w k …

… some viewpoints such as input word vectors, filter region size, the … classification method for
nursingcare text evaluation. Our nursing-care text evaluation is a supervised text classification …

… Then we evaluate their impact on text classification. To deal with the different levels of …
, word embeddings and text classification. In Sect. 3, we expose some works about word …

… Text classification is one of such standard tasks that is … languages might improve text
classification as compared to the … ) of words without word alignments either from sentencealigned …