  Representing the semantics of linguistic items in a machine-interpretable
form has been a major goal of Natural Language Processing since its earliest
days. Among the range of different linguistic items, words have attracted the
most research attention. However, word representations have an important
limitation: they conflate different meanings of a word into a single vector.
Representations of word senses have the potential to overcome this inherent
limitation. Indeed, the representation of individual word senses and concepts
has recently gained in popularity with several experimental results showing
that a considerable performance improvement can be achieved across different
NLP applications upon moving from word level to the deeper sense and concept
levels. Another interesting point regarding the representation of concepts and
word senses is that these models can be seamlessly applied to other linguistic
items, such as words, phrases and sentences.
