Term frequency–inverse document frequency (TF–IDF), one of the most popular feature
(also called term or word) weighting methods used to describe documents in the vector …

In this contemporaneous world, it is an obligation for any organization working with
documents to end up with the insipid task of classifying truckload of documents, which is the …

Document classification is a prevalent task in Natural Language Processing (NLP), which
has an extensive range of applications in the biomedical domains such as biomedical …

Feature selection (FS) is a widely used method for removing redundant or irrelevant features
to improve classification accuracy and decrease the model's computational cost. In this …

Organizations handling huge amount of data needs to preserve privacy of the documents.
Every customer has the rights to ask for privacy of their documents. These documents can be …

In this paper we perform a comparative analysis of three models for a feature representation
of text documents in the context of document classification. In particular, we consider the …

In the era of big data where data consist of images, text, audio etc., manually analysing text
is a big challenge. Over a long time, research in Natural Language Processing (NLP) has …

Text document classification is a well known theme in the field of the information retrieval
and text mining. Selection of most desired features in the text document plays a vital role in …

The paper presents an evaluation of word embedding models in clustering of texts in the
Polish language. Authors verified six different embedding models, starting from widely used …

This paper presents a semantically rich document representation model for automatically
classifying financial documents into predefined categories utilizing deep learning. The …