Natural language parsing with data-driven dependency-based frameworks has received an
increasing amount of attention in recent years, as observed in the shared tasks hosted by …

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 …

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 …

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 …

Dual decomposition, and more generally Lagrangian relaxation, is a classical method for
combinatorial optimization; it has recently been applied to several inference problems in …

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality
phrases from a text corpus and has various downstream applications including information …

We develop a learning principle and an efficient algorithm for batch learning from logged
bandit feedback. This learning setting is ubiquitous in online systems (eg, ad placement …

Vector-space word representations have been very successful in recent years at improving
performance across a variety of NLP tasks. However, common to most existing work, words …

We propose a unified neural network architecture and learning algorithm that can be applied
to various natural language processing tasks including part-of-speech tagging, chunking …

In this study, we approach the problem of distinguishing general profanity from hate speech
in social media, something which has not been widely considered. Using a new dataset …