… employed the Naive Bayes classifier for text classification on different lemmatized, stemmed
or … We consider the standard metrics for the classification performance evaluation, namely …

… word embedding which is more efficient as it can be pre-trained and fine-tuned. As a future
scope, this word … and it excels in sentiment classification, text classification, next sentence …

… classification evaluation could have been done. This fact not only poses a problem to
compare the results among different studies but also suggests that the comparison against flat …

… text representation. Briefly, we investigate which beneficial effects can be achieved for text
classification if the multi-word… In details, a multi-word extraction method is developed based on …

… extraction of text features for improving text classification dates … of classifying new text
collections, text representations more … and classification methods used and then the evaluation …

… texts. Our objective is to analyze and increase the performance of text classification on the
texts represented with word … research on text classification. In section 3, we give a summary of …

… We consider three dimensions in our extrinsic word embeddings evaluation. First, we
investigate classic embeddings as well as comparing them to contextualised embeddings in …

… Accuracy calculation is the simplest method of evaluation but … following evaluation methods
for text classification algorithms: … methods of text feature extraction: Weighted word and word …

… ) have gained considerable attention in text classification because of the … word embedding
(ie, mapping each word into a word vector), and then employ a CNN to perform classification…

… how text classification with word subspaces is performed under the MSM framework. Then,
we present the TF weighted word subspace extension in Section V. Evaluation experiments …