Natural language processing (NLP) has recently gained much attention for representing and
analyzing human language computationally. It has spread its applications in various fields …

Sentiment analysis requires a lot of information coming from different sources and about
different topics to be retrieved and fused. For this reason, one of the most important subtasks …

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for
sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) …

Linguistic sequence labeling is a general approach encompassing a variety of problems,
such as part-of-speech tagging and named entity recognition. Recent advances in neural …

Protein secondary structure (SS) prediction is important for studying protein structure and
function. When only the sequence (profile) information is used as input feature, currently the …

We propose a unified neural network architecture and learning algorithm that can be applied
to various natural language processing tasks including part-of-speech tagging, chunking …

People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes
informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented …

If we take an existing supervised NLP system, a simple and general way to improve
accuracy is to use unsupervised word representations as extra word features. We evaluate …

We aim to build and evaluate an open-source natural language processing system for
information extraction from electronic medical record clinical free-text. We describe and …

Extracting named entities in text and linking extracted names to a given knowledge base are
fundamental tasks in applications for text understanding. Existing systems typically run a …