" This book is an absolute necessity for instructors at all levels, as well as an indispensible
reference for researchers. Introducing NLP, computational linguistics, and speech …

Recent developments in language modeling have enabled large text encoders to derive a
wealth of linguistic information from raw text corpora without supervision. Their success …

This paper presents the SemEval-2013 task on multilingual Word Sense Disambiguation.
We describe our experience in producing a multilingual sense-annotated corpus for the task …

The recently proposed Skip-gram model is a powerful method for learning high-dimensional
word representations that capture rich semantic relationships between words. However …

We present a simple yet effective approach for learning word sense embeddings. In contrast
to existing techniques, which either directly learn sense representations from corpora or rely …

Supervised word sense disambiguation (WSD) systems are usually the best performing
systems when evaluated on standard benchmarks. However, these systems need annotated …

Much previous work characterizing language variation across Internet social groups has
focused on the types of words used by these groups. We extend this type of study by …

An established method for Word Sense Induction (WSI) uses a language model to predict
probable substitutes for target words, and induces senses by clustering these resulting …

Manually constructed taxonomies provide a crucial resource for many NLP technologies, yet
these resources are often limited in their lexical coverage due to their construction …

Lexical substitution, ie generation of plausible words that can replace a particular target
word in a given context, is an extremely powerful technology that can be used as a …