Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

The recent tremendous success of unsupervised word embeddings in a multitude of
applications raises the obvious question if similar methods could be derived to improve …

Text classification is a fundamental task in NLP applications. Most existing work relied on
either explicit or implicit text representation to address this problem. While these techniques …

Many current natural language processing applications for social media rely on
representation learning and utilize pre-trained word embeddings. There currently exist …

Unsupervised methods for learning distributed representations of words are ubiquitous in
today's NLP research, but far less is known about the best ways to learn distributed phrase …

Interactive visual data analysis is most productive when users can focus on answering the
questions they have about their data, rather than focusing on how to operate the interface to …

Word embeddings have advanced the state of the art in NLP across numerous tasks.
Understanding the contents of dense neural representations is of utmost interest to the …

The goal of this paper is to provide an overview of the methods that allow text
representations with a focus on embeddings for text of different lengths, specifically on works …

Definition modeling, the task of generating a definition for a given term, is a relatively new
area of research applied in evaluating word embeddings. Automatic generation of dictionary …

Abstract We introduce Multi-SimLex, a large-scale lexical resource and evaluation
benchmark covering data sets for 12 typologically diverse languages, including major …