What is Semantic Analysis in Natural Language Processing – Explore Here

The core of online communication is text, whether it is blog posts, social media, documents, group chats, or comments in a forum.

Bonus Read: Best NLP Examples

The web is full of information that a company could use to stay ahead of the competition. 

Capturing the information is not hard. What’s difficult is making sense of every word and comprehending what the text says. 

Humans can understand information simply through cognition. It is an unconscious process, but that is not the case with Artificial Intelligence. These bots cannot depend on the ability to identify the concepts highlighted in a text and produce appropriate responses. 

So how does technology understand language and text? 

What is Semantic Analysis in Natural Language Processing

Semantic analysis refers to understanding what text means. This process enables computers to identify and make sense of documents, paragraphs, sentences, and words. 

This is done by analyzing the grammatical structure of a piece of text and understanding how one word in a sentence is related to another. 

Natural Language Processing (NLP) is divided into several sub-tasks and semantic analysis is one of the most essential parts of NLP. 

Machine learning tools such as chatbots, search engines, etc. rely on semantic analysis. 

Companies use semantic analysis to extract valuable data from unstructured information. This includes emails, customer feedback, comments, and much more!

Elements of Semantic Analysis

Semantic analysis captures the real meaning of a given paragraph by first reading it and then identifying the text elements and their grammatical role. 

This means it can identify whether a text is based on “sports” or “makeup” based on the words in the text. However, even if the related words aren’t present, this analysis can still identify what the text is about. 

The following are the elements of semantic analysis:

Hyponymy:

Hyponymy refers to a generic term and its relationship with how many times it shows up in a given text. 

In such a situation, a hypernym is used to refer to the generic term while its instances are known as hyponyms.

Take the example of “plant”. In this context, this will be the hypernym while other related words that follow, such as “leaves”, “roots”, and “flowers” are referred to as their hyponyms.

Homonymy:

Two words that are spelled in the same way but have different meanings are “homonyms” of each other.

The word “bat” could refer to either the nocturnal animal or equipment used to hit a ball. 

Polysemy:

Polysemy is from the Greek language, meaning “many signs”. This could be a word or a phrase that has the same spellings but different meanings. For instance, the word bank could mean:

• Financial institution
• The building of an institution

Meronomy: 

Meronomy denotes a part of something. This is an important semantic relation that helps identify words that are part of something, for example, “finger” is a part of the “hand.”

Synonyms:

Synonyms are two or more words that are closely related because of similar meanings. For example, happy, euphoric, ecstatic, and content have very similar meanings. 

Antonyms:

Antonyms are the complete opposite of synonyms. These words have opposite meanings, such as day and night, or the moon and the sun. 

The semantic analysis also identifies signs and words that go together, also called collocations. 

Semantic machine learning algorithms can use past observations to make accurate predictions. This can be used to train machines to understand the meaning of the text based on clues present in sentences. 

What Are Semantic Analysis Extraction Models in NLP:

1. Keyword Extraction:

The process of extracting relevant expressions and words in a text is known as keyword extraction. 

This enhances the machine’s ability to understand the text. 

2. Entity Extraction: 

This refers to identifying proper nouns in a given text. For example, names of people, places, companies, etc. 

Entity extraction is a useful tool for customer service teams that can use it to identify emails, products, and other valuable information.

Conclusion:

Machine learning and semantic analysis are both useful tools when it comes to extracting valuable data from unstructured data and understanding what it means. 

This allows companies to enhance customer experience, and make better decisions using powerful semantic-powered tech.