When word senses of two different word are identical or nearly identical then those two word senses are called synonyms.
Synonym describe the relationship between senses of words rather than relationship between words.

Synonymy

When word senses of two different word are opposite in meaning then thoses two word senses are called antonyms.
Two words are antonyms if their position on a sacle or direction are oppposite.

Antonymy

Word senses or words can be related taxonomically 

Taxonomic Relations

Meronymy represents the part-whole relation.

A leg is part of a chair;


a wheel is part of a car. 

wheel is a meronym of car, and car is a holonym of wheel.

Meronymy and Holonym

Semantic relationship between senses of a word is called Structured Polysemy.

For example 
The bank is on the corner of Nassau and Witherspoon.


BUILDING ↔ ORGANIZATION


Structured Polysemy

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



Synonymy


Antonymy


Taxonomic Relations	


Meronymy


Structured Polysemy

San Jose State University

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

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

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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