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. <h3 id="what-is-nlp-or-natural-language-processing">What is NLP or Natural Language Processing?</h3>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. <h3 id="how-does-nlp-work-for-computers">How does NLP work for computers? </h3>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:<blockquote>Morphological and Lexical Analysis</blockquote><blockquote>Syntactic Analysis (Parsing)</blockquote><blockquote>Semantic Analysis</blockquote><blockquote>Pragmatic Analysis </blockquote>Morphological and Lexical AnalysisLexical 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 AnalysisTo 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 AnalysisThe 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 <h3 id="what-is-natural-language-generation-nlg">What is Natural Language Generation (NLG)?</h3>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.<h3 id="why-is-nlp-important">Why is NLP important?</h3>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. <h3 id="top-use-cases-of-nlp">Top Use Cases of NLP</h3>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. <h3 id="conclusion">Conclusion</h3>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.

Toulika Das

Everything You Need To Know About NLP

With the evolution of technology every day, the process of doing business is also changing. In recent times, many changes have taken place in e-commerce sales and customers' way of buying and selecting different products and services. From visiting a store by themselves, checking out item stock availability to purchasing unlimited products/services from online stores with a few clicks, and tracking the order status, everything has now become digital. According to the research of Statista, "the number of global digital customers will increase to 2.14 billion by the end of 2021. With these rapid changes, e-commerce industries also need to pace up with the customers' growing demand. The lack of proper customization and personalization with users reduces sales but also leads to the downfall of business' goodwill.<h3 id="what-is-conversational-ai">What is Conversational AI</h3>Conversational AI is a technology that acts as a bridge between customers and enterprises. By using enhanced technologies such as Natural Language Processing, Automatic Speech Recognition, and Machine Learning; it tries to solve the needs of both e-commerce sites and consumers. Many retail stores and e-commerce companies are shifting to various forms of conversational AI such as chatbots, messaging apps, voice assistants, visual search engines and many others to provide 24/7 support to consumers' requirements, offer additional services and leverage more sales. Conversational AI targets that chunk of consumers' experience that can be beneficial for extra lead to retail and e-commerce services.<h3 id="different-forms-of-conversational-ai-for-the-retail-and-e-commerce-industry">Different Forms of Conversational AI For The Retail and E-commerce Industry </h3>Chatbots Chatbots with Natural Language Processing (NLP) capabilities are excellent and versatile that engage with customers and provide instant answers to their queries. According to Hubspot, 47% of customers are open to make a purchase via chatbots. They can handle a large number of queries and are available 24/7 live for human interaction and user experience. It is not based on one-way communication, furthermore, it allows customers to engage in an organic way by resolving their issues by giving relevant responses.Messaging Apps The different messaging apps like WhatsApp, WeChat, Facebook Messenger, Slack and more help to make conversions with consumers easier without the need to navigate to other phone apps or sites. As per Juniper stats - over 70% of chatbots availability will be retail-based by 2023, and chatbot e-commerce transactions will stretch out to $112 billion annually.Voice Assistant Voice search option has gradually decreased the text-based search for online and e-commerce platforms, and thus, are also minimizing the consumers' efforts. The customers can use voice-based search assistants for their needs instead of keypad searches. The AI shopping bots will listen to the voice, and voice search mechanisms will provide the assistant for particular queries.Now, the question arises of how industries can use and implement Conversational AI for better productivity and efficient interaction with customers.<h3 id="ways-conversational-ai-can-improve-customer-engagement">Ways Conversational AI Can Improve Customer Engagement </h3>NLP Built Bots Bots built with Natural Language Processing understand the intent behind the customer queries and engage in more human-like conversation with users. NLP uses models that help bots to gather information to create more personalized messages for the customers. Thus, they can manage users to stay on one website for a longer duration. The use of chatbots as digital assistants on a website or any other social media platform is a prominent way of creating new buyers. Multi-lingual Bots A Multi-lingual bot uses multiple languages to converse with customers. They understand the context behind languages and, therefore, can be a powerful asset for all e-commerce and retail businesses around the globe. The use of these bots by companies makes them accessible to a vast number of users not only for purchasing various products/services but to create brand awareness among customers. By engaging in a particular language with potential customers can broaden e-commerce sales and enable them to reach a wider audience for long-term relationships and growth.Omni-channel conversation It is a multi-channel approach to sales that provides and focuses on a smooth customer experience with integrated and seamless shopping. Nowadays, customers want to save time, personalized service, knowledge about online products before ordering, track record of item delivery, trust on social media and other platforms. All these needs can be catered through omnichannel conversation to lead generation for retailers and other companies.Virtual Sales Agent Virtual selling has become the need of an hour in this situation of the Covid-19 pandemic. A virtual sales agent is an employee who works remotely or independently to communicate with customers to provide rich and reliable services. Virtual selling enables e-commerce companies to expand their sales services, promotion and marketing. The buyers are often confused regarding product pricing or features. The reliable recommendations and individual services provide value to the buyers. This allows users to choose products according to their wish that results in loyal consumers and develop trust between retailers and customers.<h3 id="advantages-of-using-conversational-ai">Advantages of Using Conversational AI</h3>Conversational AI application makes it easy to navigate different phone apps and websites for consumers to easily track and look out for what they require. It also allows convenience and deeper learning to the retail and e-commerce industry to reach their prospective customers and clients. It arranges the natural conversation to customers wherever they live across the globe over whichever platform or channel they choose. Some enterprises that use chatbots for delivering and personalized services to buyers include Sephora, Apple Watch, Domino's pizza and H&amp;M. The main pros of using Conversational AI are:<blockquote>Seamless payment method</blockquote><blockquote>Easy decision-making support</blockquote><blockquote>Fast and quick service</blockquote><blockquote>One-to-one attention</blockquote><blockquote>Real store shopping experience </blockquote>Those are the gone days of using traditional methods to increase sales and profits for your business. Several studies have shown that messages through chatbots have 85% of average opening rates with a Click-through rate (CTR) of 40%. Now, with help of this cost-effective alternative and strategy, your business can change potential buyers to revisiting consumers and can also give a boost to sales. Conversational AI provides a unique experience to the customers and can enhance a spark of interest in online engagement and shopping.

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