What is NLP: An Easy Guide For 2021

Introduction

NLP stands for Natural Language Processing. It is an Artificial Intelligence branch (AI) that explores human language comprehension by machines. The aim is to develop systems that can sense the text and carry out translation, grammatical checks, and subjects’ classification. Organizations use software equipped with NLP to obtain data insight and automate repetitive tasks. For example, this sentiment analyzer may help brands identify textual emotions such as negative social media commentary. 

But what is NLP? What is the functioning of  Natural Language Processing? What is NLP meaning? Where is the use of NLP? In this article, we break down this branch of Artificial Intelligence so that you can understand it.

Table of Contents

• What is Natural Language Processing?
• How Does NLP Work?
• What Are Different NLP Techniques?
• Use Cases of NLP

1) What is Natural Language Processing?

Computers are allowed to understand the human language through natural language processing (NLP). Voice assistants such as Google Assist, Siri, and Alexa are the most common Natural Language Processing examples in action. “Hey Alexa, where is the nearest chemist shop?” NLP recognizes and transforms this query into numbers and enables them to be read easily by computers. In short, the purpose of Natural Language Processing (NLP) is to make it simple for machines to understand human language, which is complex, nuanced, and incredibly diverse.

2) How Does NLP Work?

How does NLP work? It first uses linguistics to evaluate the grammar structure and the meaning of words, and then NLP algorithms develop intelligent systems that can accomplish various tasks. Another common NLP application is chatbots, allowing you to solve problems while performing natural language generation, in simple terms, having a conversation in plain English!

3) What Are Different NLP Techniques?

For computers to comprehend text, there are two types of Natural Language Processing techniques – 

1. Syntactic Analysis: Syntactic Analysis analyzes text using simple grammar rules to define the phrase form, the organization, and the link between words.

Its four main tasks are: 

• Tokenization is a break-up of text into smaller sections, which may be phrases or sentences, to enable text handling. 
• Part of speech tagging (PoS tagging) marks tokens like a verb, adverb, adjective, noun, etc. This action helps to evaluate the meaning of a word (e.g., “book” means many things, whether used as a noun or a substantive). 
• Lemmatization & Stemming is the reduction of inflected terms in the basic form to promote their study. 
• Stop-word removal often takes away words that do not add any semanticized meaning, including I, them, etc.

2. Semantic Analysis: Semantic Analysis aims at capturing the text’s meaning. First of all, it analyzes the meaning of each word (lexical semantics). It then examines the combination of words and their definitions in context. 

Semantic analysis’ critical subtasks are: 

• Word sense disambiguation makes it easier to identify how a word gets used within a specific context. 
• Relationship extraction tries to explain how entities (places, individuals, groups, etc.) connect in a text.

4) Use Cases of NLP

In this section of the article, we’ll discuss some Natural Language Processing applications. The NLP tools enable businesses to understand how their consumers interpret e-mails, product feedback, social media publications, surveys, and many more in all communication networks. 

AI technologies understand online interactions and how consumers communicate about companies, automate routine, time-consuming activities, improve productivity, and make it easier for employees to concentrate on work more effectively. 

Here are some core NLP applications –

• Sentiment Analysis: Sentimental analysis considers feelings in the text as optimistic, negative, and neutral opinions. By adding text to this free feeling research tool, you will see how this works. Companies may gain insight into how their consumers feel about brands or products by reviewing social media messages, product reviews, or online polls. For example, you can monitor your brand’s social media posts in real-time and automatically identify angry customers’ comments.
• Language Translation: In recent years, machine translation technology has advanced tremendously, with translations from Facebook performing in 2019 in superhuman terms. Tools for translation allow companies to communicate, develop their global communication, or reach new markets in different languages. You may also train translation instruments in any given field, such as finance or medicines, to understand particular terminology. Therefore, you need not worry about incorrect translations common to generic translation instruments.
• Text Extraction: Extraction of text helps you to extract predefined text content. This tool lets you identify and remove specific keywords and characteristics (such as product codes, colors, and specs) and named entities if you process large quantities of data (like names of people, locations, company names, emails, etc.). Companies can automatically use text extraction to identify key provisions in legal documents, recognize keywords in customer service tickets, or pull product specifications from a text paragraph, among other applications.
• Topic Classification: Classification of topics aids in arranging unstructured categories of content. It is an ideal way to get ideas from the input of customers for businesses. Imagine reviewing hundreds of open-ended NPS survey reactions. How many replies do your customer service mention? How much does the “pricing” of customers say? You will have all your data tagged in seconds using this NPS Feedback classifier.

Conclusion

In this article, we discussed many NLP basics and learned how NLP works. The part of the AI that examines the relationship between the computer and human language is Natural Language Processing (NLP). In this context, NLP works to develop tools, such as chatbots, spell-checkers, or language translators, that we use every day. NLP produces systems in conjunction with machine learning algorithms that can work alone and enhance the efficiency of interactions. NLP driven tools can support you by feeling to identify publications in social media or, among many other things, extract names from business emails.

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