Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of
data in an enormous and dynamic way. In this regard, the sentiment analysis appeared to be …

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP
tasks to capture the long-term and local dependencies, respectively. Attention mechanisms …

Long short-term memory (LSTM) neural networks and attention mechanism have been
widely used in sentiment representation learning and detection of texts. However, most of …

General language model BERT pre-trained on cross-domain text corpus, BookCorpus and
Wikipedia, achieves excellent performance on a couple of natural language processing …

Most neural machine translation (NMT) models are based on the sequential encoder-
decoder framework, which makes no use of syntactic information. In this paper, we improve …

Due to the rapid growth of textual information on the web, analyzing users' opinions about
particular products, events or services is now considered a crucial and challenging task that …

The rapid growth of social media, news sites, and blogs increases the opportunity to express
and share an opinion on the Internet. Researchers from different fields take advantage of …

Abstract Knowledge about the relations between biomedical entities (such as drugs and
targets) is widely distributed in more than 30 million research articles and consistently plays …

Sentiment Analysis has seen much progress in the past two decades. For the past few years,
neural network approaches, primarily RNNs and CNNs, have been the most successful for …

The rise of big data analytics on top of NLP increasing the computational burden for text
processing at scale. The problems faced in NLP are very high dimensional text, so it takes a …