Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

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) …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Attention-based encoder-decoder neural network models have recently shown promising
results in machine translation and speech recognition. In this work, we propose an attention …

Textual information is becoming available in abundance on the web, arising the requirement
of techniques and tools to extract the meaningful information. One of such an important …

Semantic slot filling is one of the most challenging problems in spoken language
understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for …

As one of the most comprehensive and objective ways to describe facial expressions, the
Facial Action Coding System (FACS) has recently received significant attention. Over the …

Abstract Clinical Named Entity Recognition (CNER) is a critical task which aims to identify
and classify clinical terms in electronic medical records. In recent years, deep neural …

Sequence labeling for extraction of medical events and their attributes from unstructured text
in Electronic Health Record (EHR) notes is a key step towards semantic understanding of …