Semantic Representations of Word Senses and Concepts
This work tackles the problem of semantic ambiguity in word representations for NLP practitioners, offering a more nuanced approach that enhances application performance.
The paper addresses the limitation of conflating multiple meanings into single word vectors by proposing representations for individual word senses and concepts, achieving considerable performance improvements across various NLP applications.
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.