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

In this tutorial we introduce three approaches to preprocess text data with Natural Language
Processing (NLP) and perform text document classification using machine learning. The first …

This paper introduces a convolutional sentence kernel based on word embeddings. Our
kernel overcomes the sparsity issue that arises when classifying short documents or in case …

The advancement in technology has resulted in expansion in the volume of online text
documents. It is interesting to note that in the previous two years alone, more electronic data …

Deep learning has become one of the most popular machine learning methods. The
success in the text processing, analysis and classification has been significantly enhanced …

English is a language commonly used in the international world. Meanwhile, English that is
often used in society is British English and American English. Beyond of the similarities, they …

In text mining, information retrieval, and machine learning, text documents are commonly
represented through variants of sparse Bag of Words (sBoW) vectors (eg TF-IDF [1]) …

Term weighting scheme has significant effects on the text classification performance. The
main reason is that in text classification tasks, term weighting scheme determines the way in …

Various studies have demonstrated that convolutional neural networks (CNNs) can be
directly applied to different levels of text embedding, such as character‐, word‐, or document …

While machines can discover semantic relationships in natural written language, they
depend on human intervention for the provision of the necessary parameters. Precise and …