Word representation has always been an important research area in the history of natural
language processing (NLP). Understanding such complex text data is imperative, given that …

Polysemy is the type of lexical ambiguity where a word has multiple distinct but related
interpretations. In the past decade, it has been the subject of a great many studies across …

Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …

Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they
represent when the word is polysemous. Here it is shown that multiple word senses reside in …

Abstract Word Sense Disambiguation models exist in many flavors. Even though supervised
ones tend to perform best in terms of accuracy, they often lose ground to more flexible …

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

Owing to the need for a deep understanding of linguistic items, semantic representation is
considered to be one of the fundamental components of several applications in Natural …

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI.
However, when it comes to Natural Language Processing (NLP), symbols have to be …

Sentiment analysis on large-scale social media data is important to bridge the gaps between
social media contents and real world activities including political election prediction …

Accurate semantic representation models are essential in text mining applications. For a
successful application of the text mining process, the text representation adopted must keep …