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2. Homonyms.
3. Polysemy.
4. Synonyms.
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6. Antonyms.
7. Meronomy."
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Semantic analysis

What is semantic analysis?

Semantic analysis is a sub-task of NLP. It uses machine learning and NLP to understand the real context of natural language. Search engines and chatbots use it to derive critical information from unstructured data, and also to identify emotion and sarcasm.

You can look at semantic analysis as the process of extracting meaning from text. It analyzes the grammatical structure of the text, looking for patterns and relationships between words in a specific text.

What are the elements of semantic analysis?

  • Hyponyms:
    These are specific lexical items of generic lexical items. The generic lexical items are called hypernyms. As an example, ‘crow’ would be a hyponym of the hypernym ‘bird’.
  • Homonyms:
    These are words that are spelled identically but have different meanings. An example of homonyms would be ‘book’ (something that you read) and ‘book’ (the act of placing a reservation).
  • Polysemy:
    This refers to a situation where words are spelt identically but have different but related meanings. An example of polysemy could be ‘drink’. The mean could change depending on whether we are talking about a drink being made by a bartender or the actual act of drinking something.
  • Synonyms:
    Words that have the exact same or very similar meanings as each other. Examples could include ‘beautiful’ and ‘gorgeous’.
  • Antonyms:
    Words that have opposite meanings. Examples include ‘dull’ and ‘bright’.
  • Meronomy:
    An arrangement of words that suggests that something is a part of a whole. An example could include ‘a slice of pizza.’

Semantic analysis also takes collocations (words that are habitually juxtaposed with each other) and semiotics (signs and symbols) into consideration while deriving meaning from text.

Why is meaning representation needed?


Here are a few reasons why meaning representation is necessary:

  • Firstly, meaning representation allows us to link linguistic elements to non-linguistic elements.
  • Meaning representation also allows us to represent unambiguous, canonical forms at their lexical level.
  • It can even be used for reasoning and inferring knowledge from semantic representations.

How does semantic analysis represent meaning?

Here are the approaches that semantic analysis uses for the purpose of meaning representation:

  • Frames
  • Semantic nets
  • First order predicate logic (FOPL)
  • Case grammar
  • Conceptual dependency (CD)
  • Conceptual graphs
  • Rule-based architecture


Techniques for semantic analysis

There are two techniques for semantic analysis that you can use, depending on the kind of information you  want to extract from the data being analyzed. These include text classification and text extraction.

Here are some semantic classification models

  • Intent classification models classify text based on the kind of action that a customer would like to take next. They look at the customer’s intent.
  • Sentiment analysis involves identifying emotions in the text to suggest urgency.
  • Topic classification is all about looking at the content of the text and using that as the basis for classification into predefined categories.


Here are two semantic extraction models

  • Entity extraction looks for entities in the text. Entities could include names of companies, products, places, people, etc.
  • Keyword extraction focuses on searching for relevant words and phrases. It is usually used along with a classification model to glean deeper insights from the text.
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Semantic analysis

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is semantic analysis?

Semantic analysis is a sub-task of NLP. It uses machine learning and NLP to understand the real context of natural language. Search engines and chatbots use it to derive critical information from unstructured data, and also to identify emotion and sarcasm.

You can look at semantic analysis as the process of extracting meaning from text. It analyzes the grammatical structure of the text, looking for patterns and relationships between words in a specific text.

What are the elements of semantic analysis?

  • Hyponyms:
    These are specific lexical items of generic lexical items. The generic lexical items are called hypernyms. As an example, ‘crow’ would be a hyponym of the hypernym ‘bird’.
  • Homonyms:
    These are words that are spelled identically but have different meanings. An example of homonyms would be ‘book’ (something that you read) and ‘book’ (the act of placing a reservation).
  • Polysemy:
    This refers to a situation where words are spelt identically but have different but related meanings. An example of polysemy could be ‘drink’. The mean could change depending on whether we are talking about a drink being made by a bartender or the actual act of drinking something.
  • Synonyms:
    Words that have the exact same or very similar meanings as each other. Examples could include ‘beautiful’ and ‘gorgeous’.
  • Antonyms:
    Words that have opposite meanings. Examples include ‘dull’ and ‘bright’.
  • Meronomy:
    An arrangement of words that suggests that something is a part of a whole. An example could include ‘a slice of pizza.’

Semantic analysis also takes collocations (words that are habitually juxtaposed with each other) and semiotics (signs and symbols) into consideration while deriving meaning from text.

Why is meaning representation needed?


Here are a few reasons why meaning representation is necessary:

  • Firstly, meaning representation allows us to link linguistic elements to non-linguistic elements.
  • Meaning representation also allows us to represent unambiguous, canonical forms at their lexical level.
  • It can even be used for reasoning and inferring knowledge from semantic representations.

How does semantic analysis represent meaning?

Here are the approaches that semantic analysis uses for the purpose of meaning representation:

  • Frames
  • Semantic nets
  • First order predicate logic (FOPL)
  • Case grammar
  • Conceptual dependency (CD)
  • Conceptual graphs
  • Rule-based architecture


Techniques for semantic analysis

There are two techniques for semantic analysis that you can use, depending on the kind of information you  want to extract from the data being analyzed. These include text classification and text extraction.

Here are some semantic classification models

  • Intent classification models classify text based on the kind of action that a customer would like to take next. They look at the customer’s intent.
  • Sentiment analysis involves identifying emotions in the text to suggest urgency.
  • Topic classification is all about looking at the content of the text and using that as the basis for classification into predefined categories.


Here are two semantic extraction models

  • Entity extraction looks for entities in the text. Entities could include names of companies, products, places, people, etc.
  • Keyword extraction focuses on searching for relevant words and phrases. It is usually used along with a classification model to glean deeper insights from the text.
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