… of document collections in Section 4, including the analysis of our document embeddings …
The experimental evaluation of the proposed representations in the text classification task is …

… text preprocessing and bag-of-words representation. We demonstrate that document Embeddings
outperform text … a simple method for document Embedding construction. Moreover, the …

… kinds of embeddings, like document embeddings and … embeddings can contribute to text
classification. First, Sect. 3.1 exemplifies the potential benefits of semantics in text classification …

… well with classic embeddings for document datasets like … selecting word embeddings for
text classification in deep models, … a long document for improving classification performance, …

… document embeddings for the improvement on the accuracy of the algorithms in classifying
these documents [… of the text classification tasks and has become a standard baseline for new …

… Therefore, it is effective even for document classification, which is addressed in this study. …
the classification of documents using BERT. In applying BERT to classification problems, …

… on the subject of neural embeddings. We conclude that the … document embedding
models that are trained using unlabeled data to maNe use of labeled data for text classification …

… of the usage of word embeddings in more general, multi-… -called document embeddings
directly in TC task, that aims to represent a document as a combination of the word embeddings …

… affects the performance of text classification and we investigate … the embedding models such
as (doc2vec) in the task of text … the document embeddings of one of the embedding methods…

… Word embeddings are useful in feature generation and Natural Language Processing
tasks like text classification, document clustering, and sentiment classification. Word …