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 …

During real-time language comprehension, our minds rapidly decode complex meanings
from sequences of words. The difficulty of doing so is known to be related to words' …

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 …

Many natural language processing tasks, including the most advanced ones, routinely start
by several basic processing steps–tokenization and segmentation, most likely also POS …

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 …

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 …

Syntactic constituency parsing is a fundamental problem in naturallanguage processing
which has been the subject of intensive researchand engineering for decades. As a result …

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the
brain deal successfully with the continual deluge of linguistic input? We argue that, to deal …

Text-driven human motion generation in computer vision is both significant and challenging.
However, current methods are limited to producing either deterministic or imprecise motion …

We present a new edition of the Google Books Ngram Corpus, which describes how often
words and phrases were used over a period of five centuries, in eight languages; it reflects …