A detailed guide to NLP

 Have you ever wondered why your chatbot doesn't understand you sometimes? Well the answer is non-existence or lack of NLP (Natural Language Processing).

What is Natural Language Processing?

NLP is a sub-category of artificial intelligence, information engineering, computer science, and linguistics that helps the machines to understand the human language. It helps in analyzing the data that humans refrain from doing but can be of great potential.

In simple terms, NLP helps to understand plenty of data and analyze it to make it easy for computers to communicate with humans using NLP machine learning, deep learning, and AI technologies

A beginners guide to Natural language processing

Natural language is the most natural interface between a user and a machine. In the ideal case, this involves speech recognition and voice generation. Even Alan Turing recognized this in his “intelligence” article, in which he defined the “Turing test” as a way to test a machine’s ability to exhibit intelligent behavior through a natural language conversation.

However, it depends widely upon Artificial Intelligence, Machine Learning, and Deep Learning that help the whole process of the NLP.

With an AI-platform like KaptureCRM, you can start using pre-trained models right away, or build a customized NLP solution in just a few steps.

Every business wants to incorporate NLP into your business module therefore, it is essential to understand what are applications of NLP?

• Sentiment Analysis

Sentiment analysis, however, is able to recognize subtle nuances in emotions and opinions ‒ and determine how positive or negative they are.

• Chatbots & Virtual Assistants

Chatbots and virtual assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation.

• Text Classification

Text classification, a text analysis task that also includes sentiment analysis, involves automatically understanding, processing, and categorizing unstructured text.

• Text Extraction

Text extraction, or information extraction, automatically detects specific information in a text, such as names, companies, places, and more. This is also known as named entity recognition.

• Machine Translation

Machine translation (MT) is one of the first applications of natural language processing. Even though Facebooks’s translations have been declared superhuman, machine translation still faces the challenge of understanding context.

• Text Summarization

Automatic summarization is pretty self-explanatory. It summarizes text, by extracting the most important information. Its main goal is to simplify the process of going through vast amounts of data, such as scientific papers, news content, or legal documentation.

• Market Intelligence

Marketers can benefit from natural language processing to learn more about their customers and use those insights to create more effective strategies.

• Auto-Correct

Natural Language Processing plays a vital role in grammar checking software and auto-correct functions. Tools like Grammarly, for example, use NLP to help you improve your writing, by detecting grammar, spelling, or sentence structure errors.

• Intent Classification

Intent classification consists of identifying the goal or purpose that underlies a text. Apart from chatbots, intent detection can drive benefits in sales and customer support areas.

• Urgency Detection

NLP techniques can also help you detect urgency in text. You can train an urgency detection model using your own criteria, so it can recognize certain words and expressions that denote gravity or discontent.

• Speech Recognition

Speech recognition technology uses natural language processing to transform spoken language into a machine-readable format.

In technical parlance, NLP is a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements, helps in creating and reading textual data, visual data, and more. Basically, it helps computers understand, interpret, and manipulate human language. From automatic translation or sentence completion to identify insurance fraud and powering chatbots, NLP is used almost everywhere. The main objective of NLP applications is to help humans have interaction with computers as they would with another human.

Advantages of using NLP:

• Enable Natural Conversations

NLP utilizes both machine learning and fundamental meaning in order to maximize useful outcomes and enable natural conversations. NLP chatbots can understand language and speech structures, track morphemes across languages, and even interpret language idioms and slang to derive meaning.

• Allocate Human Resources Effectively

Along with natural conversations, chatbots trained with NLP are particularly good at doing the little things so that human operators can focus on mission-critical tasks. NLP chatbots dramatically decrease the human effort needed for manual and repetitive tasks.

• Reduce Costs and Inefficiencies

NLP-trained chatbots can help you to dramatically reduce costs associated with manual and repetitive tasks. Businesses will benefit even more in the future as machine learning improves chatbot capacity and people become more accustomed to dealing with machines.

• Improve Customer Satisfaction

Chatbots can address multiple customers simultaneously, which means no waiting time and no frustrated customers. NLP trains chatbots to understand, analyze, and prioritize user questions based on intent and context. This allows chatbots to respond to customer queries faster than a human being.

• Benefit from Market Research and Analysis

NLP chatbots and software systems can perform a valuable role in market research and analytics. From social media comments and customer reviews through to internal and external search queries, mapping information and making sense of raw information is the perfect job for an NLP-based chatbot.

NLP is a game-changing technology that will shape the future of the Internet.