Neural networks are a family of powerful machine learning models. This book focuses on the
application of neural network models to natural language data. The first half of the book …

This paper presents a sequence to sequence (seq2seq) dependency parser by directly
predicting the relative position of head for each given word, which therefore results in a truly …

In this paper, we propose a neural network model for graph-based dependency parsing
which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual information instead of …

We present fast, accurate, direct nonprojective dependency parsers with thirdorder features.
Our approach uses AD3, an accelerated dual decomposition algorithm which we extend to …

Dependency parsers are among the most crucial tools in natural language processing as
they have many important applications in downstream tasks such as information retrieval …

Conventional graph-based dependency parsers guarantee a tree structure both during
training and inference. Instead, we formalize dependency parsing as the problem of …

We present a novel approach, called selectional branching, which uses confidence
estimates to decide when to employ a beam, providing the accuracy of beam search at …

Accurate scoring of syntactic structures such as head-modifier arcs in dependency parsing
typically requires rich, highdimensional feature representations. A small subset of such …

We propose the first implementation of an infinite-order generative dependency model. The
model is based on a new recursive neural network architecture, the Inside-Outside …

Semantic role labeling is primarily used to identify predicates, arguments, and their semantic
relationships. Due to the limitations of modeling methods and the conditions of pre-identified …