WordNets words contain gloss which is a dictionary style definition for the word.

Gloss

Synsets are near-synonyms for WordNets which represents words as lists
of the word senses that can be used to express the concept of the word.

Synset

Fine-grained word senses in WordNets require labels for each synset with a lexicographic category drawn from a sematic field. These categories are supersenses, because of their specifc grouping of senses.

Supersenses

WordNet is a large online thesaurus —a database that represents word senses—with versions in many languages. WordNet also represents relations between senses.

University of Michigan - Ann Arbor

A sense (or word sense) is a discrete representation of one aspect of the meaning of a word.

Word Senses

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

WordNet

One practical technique for determining if two senses are distinct is to conjoin two uses of a word in a single sentence; this kind of conjunction of antagonistic readings is called zeugma.

Zeugma

1. Giving textual definitions for each sense called glosses.
-Despite their circularity and lack of formal representation, glosses can still be useful for computational modeling of senses.

2. Defining a sense through its relationship with other senses.

Two Ways to Define Senses

There are some relations between word senses which need crucial computational analysis and they are



Synonymy


Antonymy


Taxonomic Relations	


Meronymy


Structured Polysemy

Relations Between Senses

Word sense disambiguation (WSD) is the task of selecting the correct sense for a word. WSD algorithms take as input a word in context and a fixed inventory of potential word senses and outputs the correct word sense in context.
- In some situations, we just need to disambiguate a small number of words. In such lexical sample tasks, we have a small pre-selected set of target words and an inventory of senses for each word from some lexicon. Since the set of words and the set of senses are small, simple supervised classification approaches work very well. 
- More commonly, however, we have a harder problem in which we have to disambiguate all the words in some text. In this all-words task, the system is given an entire texts and a lexicon with an inventory of senses for each entry and we have to disambiguate every word in the text (or sometimes just every content word).

Word Sense Disambiguation

In unsupervised approaches, we don’t use human-defined word senses. The set of “senses” of each word is created automatically from the instances of each word in the training set.

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