Table of contents
1.
Introduction
2.
Parts Of Semantic Analysis
3.
Lexical Semantics
3.1.
Example:
4.
Semantic Analysis Techniques
4.1.
Text Classification
4.2.
Text Extraction
5.
Significance Of Semantic Analysis
6.
FAQs
7.
Key Takeaways
Last Updated: Mar 27, 2024
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Lexical Semantics

Author Mayank Goyal
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Introduction

Semantic Analysis is a branch of Natural Language Processing (NLP) concerned with deciphering the meaning of natural language. As humans, understanding Natural Language may appear to be a simple procedure. However, machines interpreting human language is complex due to the tremendous complexity and subjectivity. Semantic Analysis of Natural Language captures the meaning of a document by considering context, logical sentence structure, and grammar roles.

As a result, the semantic analysis aims to extract the text's exact or dictionary meaning. A semantic analyzer's job is to ensure that the text is meaningful.

Parts Of Semantic Analysis

Natural language semantic analysis can be divided into two categories:

1. Lexical Semantic Analysis: Lexical Semantic Analysis entails deciphering the meaning of each word in the text. A word in the text has been delegated to carry the dictionary.

2. Compositional Semantics Analysis: While knowing the meaning of each word in the text is essential, it is not enough to fully comprehend the text's meaning.

Consider the following two statements as an example:

Sentence 1: Coding Ninjas are extremely popular among students.

Sentence 2: Students are adored by Coding Ninjas.

Even though sentences 1 and 2 include the exact root words (student, love, Coding Ninjas), their meanings are entirely different.

As a result, we strive to comprehend how individual words combine to generate the meaning of the text in Compositional Semantics Analysis.

Lexical Semantics

It's the initial step in semantic analysis, where we look at the meaning of individual words. All included words, sub-words, affixes (sub-units), compound words, and phrases. All words, sub-words, and other lexical items are lexical items. In simple terms, lexical semantics is the link between lexical elements, sentence meaning, and sentence structure.

The following are the stages that must be followed when performing lexical semantics:

  • Lexical item classification.
  • Lexical item decomposition.

There are also comparisons and differences between distinct lexical-semantic structures.

Example:

Let's use the word forward as an example.

Let's have a look at two sentences.

"Forward into the ocean," says the narrator.

"She was leaning forward."

In both of these circumstances, we can split forward.

The words' Forward' and 'forward' have two different meanings from other words. "Forward into the ocean," says the narrator. It is a statement in which the forward refers to the word 'ocean,' which is connected. "She was leaning forward." This, on the other hand, is a past tense action and refers to 'she.'

These are distinct based on the English language, but how are these semantics represented? How can we tell the difference between these two?

Other words can be seen as being related to that one term.

Semantic Analysis Techniques

Semantic Analysis can be applied in various ways depending on the final aim in mind. The following are two of the most common Semantic Analysis techniques:

Text Classification

The goal of text classification is to label the text based on the information we want to extract from the textual input.

Consider the following scenario:

We aim to classify the text with the most dominant emotion it conveys in Sentiment Analysis. It comes in handy when looking for ways to boost client reviews.

We try to categorize our material into predetermined groups using Topic Classification and, for instance, determining whether a research paper is in the fields of physics, chemistry, or mathematics.

We try to figure out what a text message's intent is in Intent Classification—for instance, determining whether an e-mail sent to customer service is a question, a complaint, or a request.

Text Extraction

We use in-text extraction to extract specific information from our content.

For example, We aim to get the crucial words that define the entire document using Keyword Extraction. We try to get all of the entities associated in a paper using Entity Extraction.

Significance Of Semantic Analysis

Natural Language Processing relies heavily on semantic analysis (NLP). Businesses must derive insights from textual data in the ever-expanding era of textual information. Semantic Analysis aids machines in deciphering the meaning of texts and extracting usable data, resulting in invaluable data while minimizing manual labor.

Furthermore, Semantics Analysis is commonly used to aid the operations of automated responding systems, such as chatbots, which respond to user queries without human participation.

FAQs

1. Why is it necessary to research lexical semantics?
One of the essential semantic relations in deciphering the meanings of words in the English language is lexical relations. They're primarily employed to examine the meanings of words in connection to one another within phrases.

2. What's the difference between lexical and semantic?
A lexical field is a set of related words representing a piece of reality. A semantic field's words all have the same semantic attribute. The subject matter, such as body parts, landforms, diseases, colors, foods, or family relationships, is frequently used to define fields.

3. What do the various elements of lexical-semantic analysis entail?
The semantics of Lexical Terms include words, sub-words, affixes (sub-units), compound words, and phrases.

4. What is the purpose of lexical semantics?
Lexical semantics is concerned with the intrinsic characteristics of word meaning, semantic relationships between words, and how word meaning is related to syntactic structure.

Key Takeaways

Let us brief out the article.

Firstly, we saw the basics of semantic analysis and parts of semantic analysis,i.e., lexical and composite semantic analysis. Later, we had a detailed discussion of lexical semantics with the help of an example. Lastly, we looked into different semantic analysis techniques with their significance. That's the end of the article.

I hope you all like it.

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