Everything You Need To Know About NLP

Sep 30, 2021
5 min read

Are you a human? Don’t worry. This is not another Captcha test. If you're reading this blog and comprehending what I'm saying, it implies that we're communicating. You and I are human beings who have specific languages to converse in. Whether it's English, German, Russian, or any other language, each has its own set of norms for conveying information. We even communicate via images, signs or symbols. But have you wondered how humans communicate with machines, or more specifically, computers?

The main method involved in helping computers to understand human language is - Natural Language Processing (NLP). In this blog, we will learn about what is NLP, how it works and discuss its various applications or use cases.

What is NLP or Natural Language Processing?

Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with computational linguistics or interactions between computers and people. The primary objective of NLP is to be able to interpret, decode, comprehend, and simply grasp human languages in a useful way. Without any set of structured data, a set of commands and formal instructions, it is next to impossible for computers to comprehend human language.

This is exactly why artificial intelligence and machine learning are getting traction. In order to infer meaning from human languages, the majority of the NLP approaches rely on machine learning.

How does NLP work for computers?

NLP is the technique of using algorithms to discover and obtain natural linguistic knowledge from unorganized language input so that computers can interpret it.

Whenever the computer receives the message, it will use algorithms to retrieve the context related to each sentence and gather the required data.

There are some primary methods used to perform Natural Language Processing activities. They are as follows:

Morphological and Lexical Analysis
Syntactic Analysis (Parsing)
Semantic Analysis
Pragmatic Analysis

Morphological and Lexical Analysis

Lexical analysis is the act of attempting to decipher what phrases mean, determine their context, and observe how one phrase relates to another. It's frequently the starting point for many NLP workflows. Lexical analysis can occur in many ways.

Syntactic Analysis (Parsing)

Syntactic analysis, often known as syntax analysis or parsing, is the practice of using official grammar principles to analyse natural language. Specific words are not subject to grammatical rules; instead, classifications and groupings of words are. Syntactic analysis is the process of assigning semantic meaning to a piece of text.

Semantic Analysis

To grasp the true meaning of any document, semantic analysis of natural language material begins with reading all of the words in the content. It recognises literary components and allocates logical and grammatical roles to them. It also recognises the connections between various topics in the text.

Pragmatic Analysis

The process of retrieving important data from text includes a step called pragmatic analysis. In NLP, pragmatic analysis is a collection of conceptual and grammatical techniques

What is Natural Language Generation (NLG)?

Natural language generation, or NLG, is a type of natural language processing that involves evaluating large amounts of data and utilising it as an input to generate text automatically. It can produce automatic responses, send emails, and even write books.

Why is NLP important?

NLP is a technology that allows machines like computers to intelligently analyse, evaluate, and draw inferences from human speech. This is far more advanced than the most contemporary chatbots and intelligent digital assistants. Natural language processing technologies may be found in a variety of places, including performing search queries, digital translation, spam protection, filtering and spell correction.

Natural language processing enables computers to converse with individuals in their native tongue and handle other speech-related activities. NLP allows computers to comprehend text, detect voice, analyse it, assess emotion, and identify which portions are essential, for instance.

Top Use Cases of NLP

Fundamental instances of Natural Language Processing or NLP are more common in our daily lives than you can imagine. Some of the top use cases of NLP are listed below.

Email Filters
One of the most fundamental and early applications of NLP online is email filtering. It began with spam filters, which identified specific words or phrases that indicate a spam message. But, like previous NLP versions, email filtering has improved. Gmail's email categorization is among the most common, recent implementations of NLP. Based on the contents of emails, the algorithm determines whether they belong in one of three categories (main, social, or promotional).

Predictive Text or Sentence completion
Text prediction is one of NLP's tasks. A sentence or series of words is submitted to a prediction machine in a text prediction problem. The algorithm's job is to show the user the phrase or phrases that have the best chance of matching the original sequence. The autocorrect or autofill feature in your smartphone’s keyboard is one of the examples of this NLP characteristic.

Autofill and predictive text follow a similar pattern to search engines in that they anticipate what you'll say depending on what you write, either by completing the word or recommending a similar one. And autocorrect will occasionally modify phrases to make the general message more comprehensible. The more you use predictive text, the more it adapts to your unique linguistic specificities.

Grammar/spelling check
In Natural Language Processing (NLP), grammar check or spelling correction are well-known jobs. The principles of grammar correction fall under the syntactic analysis of NLP. Similar to learning data for automated spelling correction, most techniques incorporate parallel datasets of erroneous and accurate word mappings from multiple sources.

Machine Translation
The objective is for computers to be able to process or "understand" natural language so that they can do activities such as translation of different languages and question responses. The wonderful part about machine translation in NLP is that it can quickly convert big chunks of text. When Google launched its automatic translation service, the majority of us were introduced to it. Custom models of translators can be used for different domains to increase the accuracy of the tasks.

Chatbots using NLP
NLP algorithms are being used by organizations to manage time-consuming and laborious operations in aspects such as customer care. As a consequence, procedures are more streamlined, and employees have more opportunities to focus on what counts most: providing excellent customer service.

Chatbots are progressively being used by customer service staff to answer basic questions. This lowers expenses and allows support employees to concentrate on more sensitive and engaging activities that need greater customization. Chatbots also help in reducing client wait times.

Smart assistants
Voice recognition software, thanks to NLP and machine learning, allows smart assistants like Apple's Siri and Amazon's Alexa to understand what we’re saying. They look for patterns in the language spoken by us, derive meaning from the same and give a meaningful answer. We have grown accustomed to saying "Hey Siri" and asking a question. Almost instantly, Siri recognizes our voice, understands what we are saying and responds with appropriate responses depending on the context of our query or command.

Data analytics
As more business intelligence (BI) vendors are offering a natural language gateway to data visualisations, natural language features are being incorporated into the workflows of data analytics. Smarter optical sequences, for example, provide the optimal visualisation for the correct job depending on the data's interpretation.

People will have more chances to examine their information utilising natural language syntax or enquiry segments. These queries are usually made up of many phrases that may be analysed and allocated a meaning as a result of this. Using language to explore data not only improves accessibility but also reduces the barrier to analytics across businesses, further than the usual analyst and software development group.

Conclusion

From communicating with computer systems through punch cards that required an entire team of people, to simply saying, “Alexa, turn down the volume”, computer interaction has come a long way. With the sophistication of artificial intelligence and machine learning, NLP will become more exceptional. NLP plays an extremely vital role in machine-human interaction. With the ongoing research in this field, more advances in NLP are expected in the near future.