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 …

Almost all current dependency parsers classify based on millions of sparse indicator
features. Not only do these features generalize poorly, but the cost of feature computation …

Abstract Abstract Meaning Representation (AMR) is a semantic formalism for which a
growing set of annotated examples is available. We introduce the first approach to parse …

We introduce a novel architecture for dependency parsing:\emph {stack-pointer
networks}(\textbf {\textsc {StackPtr}}). Combining pointer networks~\citep …

Kernel techniques have long been used in SVM to handle linearly inseparable problems by
transforming data to a high dimensional space, but training and testing large data sets is …

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

We assume that the tokenization of a sentence is fixed and known at parsing time. That is to
say that dependency parsers will always operate on a pre-tokenized input and are not …

Word representations have proven useful for many NLP tasks, eg, Brown clusters as
features in dependency parsing (Koo et al., 2008). In this paper, we investigate the use of …

Each year the Conference on Computational Natural Language Learning (CoNLL) 1
features a shared task, in which participants train and test their systems on exactly the same …