Abstract Recently, Word Embeddings have been introduced as a major breakthrough in
Natural Language Processing (NLP) to learn viable representation of linguistic items based …

In this paper, we conducted an experiment to build a classification model that combines
different techniques in most of the Natural Language Processing Tasks. We used the word …

Word embeddings act as an important component of deep models for providing input
features in downstream language tasks, such as sequence labelling and text classification …

In this paper, we propose a novel representation for text documents based on aggregating
word embedding vectors into document embeddings. Our approach is inspired by the Vector …

Despite that pre-trained word embedding models have advanced a wide range of natural
language processing applications, they ignore the contextual information and meaning …

Document representation with outlier tokens exacerbates the classification performance due
to the uncertain orientation of such tokens. Most existing document representation methods …

Word embedding and document embedding representations have proved good
performance in several natural language tasks such as information retrieval, text …

In this paper, we present Bengali word embeddings and it's application in the classification
of news documents. Word embeddings are multi-dimensional vectors that can be created by …

The relationship between words in a sentence often tells us more about the underlying
semantic content of a document than its actual words, individually. In this work, we propose …

Following the recent increase in the amount of available data, Deep Learning has become
the most popular branch of Machine Learning. This trend can also be seen in Natural …