Over the last several years, the field of natural language processing has been propelled
forward by an explosion in the use of deep learning models. This article provides a brief …

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 …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a
sentence. It is typically regarded as an important step in the standard NLP pipeline. As the …

We propose a technique for learning representations of parser states in transition-based
dependency parsers. Our primary innovation is a new control structure for sequence-to …

We present a simple and effective scheme for dependency parsing which is based on
bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector …

The current neural network models for event detection have only considered the sequential
representation of sentences. Syntactic representations have not been explored in this area …

Recent years have seen the proposal of a number of neural architectures for the problem of
Program Induction. Given a set of input-output examples, these architectures are able to …

In the deep learning (DL) era, parsing models are extremely simplified with little hurt on
performance, thanks to the remarkable capability of multi-layer BiLSTMs in context …

We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised
method for discovering syntax that simultaneously learns representations for constituents …