Recent years have seen the success of applying word embedding algorithms to natural
language processing (NLP) tasks. Most word embedding algorithms only produce a single …

As a well-known Natural Language Processing (NLP) task, text classification can be defined
as the process of categorizing documents depending on their content. In this process …

Learning word embeddings on large unla-beled corpus has been shown to be successful in
improving many natural language tasks. The most efficient and popular approaches learn or …

In this paper, based on the multilingual morphological analyzer, we researched the similar
low-resource languages, Uyghur and Kazakh, short text classification. Generally, the online …

Text classification (TC) concerns automatically assigning a class (category) label to a text
document, and has increasingly many applications, particularly in the domain of organizing …

Tokenization is an important early step in natural language processing (NLP) tasks. The
idea is to split the input sentence into smaller units, called tokens, for further processing …

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

Several studies have shown that the use of embeddings improves outcomes in many
Natural Language Processing (NLP) activities, including text categorization. This paper …

This paper introduces a set of new approaches for text representation for automatic
classification of Arabic textual documents. These approaches are based on combining the …

Arabic, like other highly inflected languages, encodes a large amount of information in its
morphology and word structure. In this work, we propose two embedding strategies that …