Continuous word representations, trained on large unlabeled corpora are useful for many
natural language processing tasks. Popular models that learn such representations ignore …

Deep Learning and Big Data analytics are two focal points of data science. Deep Learning
models have achieved remarkable results in speech recognition and computer vision in …

We describe a simple neural language model that relies only on character-level inputs.
Predictions are still made at the word-level. Our model employs a convolutional neural …

Sentiment analysis of short texts such as single sentences and Twitter messages is
challenging because of the limited contextual information that they normally contain …

Neural machine translation has recently achieved impressive results, while using little in the
way of external linguistic information. In this paper we show that the strong learning …

Distributed word representations have recently been proven to be an invaluable resource for
NLP. These representations are normally learned using neural networks and capture …

Vector-space word representations have been very successful in recent years at improving
performance across a variety of NLP tasks. However, common to most existing work, words …

We present several modifications of the original recurrent neural network language model
(RNN LM). While this model has been shown to significantly outperform many competitive …

Understanding human language has been a sub-challenge on the way of intelligent
machines. The study of meaning in natural language processing (NLP) relies on the …

Most word embedding methods take a word as a basic unit and learn embeddings
according to words' external contexts, ignoring the internal structures of words. However, in …