… features [3]. Concept based models(CBM) with natural language processing techniques used
noun-verb structures for useful feature extraction. … filtering and text classification were used …

… text classification is to classify texts of interest into appropriate classes [14]. Typical text
classification framework consists of a feature extraction … information from raw text documents, and …

… text representation on the performances of text classification. Firstly, a practical method is
proposed to implement the multi-word extraction … features when using SVM for text classification…

… and extract scene text characters in various patterns, our algorithm combines layout analysis
and text classification. … to extract more discriminative appearance features of text characters. …

… “Support vector machines and word2vec for text classification with semantic features”. In:
Cognitive Informatics & Cognitive Computing (ICCI* CC), 2015 IEEE 14th International …

… high semantic understanding of the underlying text. … feature extraction. Adding word2vec
model along with random forest classifier has proven to give better results for text classification …

… text classification, process of different term weighing methods and comparison between
different classification … of preprocessing, feature extraction, and ultimately text classification. …

… feature extraction for text classification. Firstly, we assume that we can synthesize new features
from the original features … , and that the number of new features is equal to the number of …

… So far, text classification has been successfully applied to various domains such as topic
detection [15], spam e-mail filtering [16], SMS spam filtering [17], author identification[18], web …

… -based method of text classification and the fastText method based on the concept of
word-embedding. In the first part we deal with generation of features from Polish language texts, as …