… 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 …

… 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 …

… Third, we analyze the relation between the word deletion evaluation measures and the …
There is a strong correlation between the prediction confidence and the word deletion evaluation …

… In this study, the researcher used the cosine similarity function between a pair of text
documents depends upon the word frequency and number of common words in both …

… ) 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…

… 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 …

… for text classification based on clustering word embeddings, … word in a collection of documents
is represented as word … We use a 10-fold cross-validation procedure in the evaluation. …

… 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 …

… The most important aspect for evaluation of a large amount of text data is to classify them.
So, we emphasize text classification, which is one of the most often used methods in text …