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 …

How to transfer the semantic information in a sentence to a computable numerical
embedding form is a fundamental problem in natural language processing. An informative …

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 …

In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of
the skip-gram model by explicitly distinguishing left and right context in word prediction. In …

Context representations are central to various NLP tasks, such as word sense
disambiguation, named entity recognition, coreference resolution, and many more. In this …

Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge
amount of reviews, sentiment analysis plays a basic role to extract significant information …

We present SimLex-999, a gold standard resource for evaluating distributional semantic
models that improves on existing resources in several important ways. First, in contrast to …

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 …

We consider the problem of learning general-purpose, paraphrastic sentence embeddings
based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We …

In this work, we present a novel counter-fitting method which injects antonymy and
synonymy constraints into vector space representations in order to improve the vectors' …