The replication of human intellectual processes by machines particularly computer systems is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and machine vision are examples of AI applications. Learning, reasoning, and self-correction are the three cognitive capabilities that AI programming focuses on. AI is essential because it may provide businesses with previously unavailable insights into their operations and because in some circumstances AI can do tasks better than people. AI technologies frequently perform work quickly and with minimal errors especially when it comes to repetitive, detail-oriented activities like reviewing huge quantities of legal papers to verify important fields are filled correctly.
Natural Language Processing or NLP is the automated manipulation of natural language by software such as speech and text. It’s where computer science, languages, and machine learning merge. The area focuses on natural language communication between computers and humans and NLP is all about teaching computers to comprehend and produce human language. Natural language processing has been studied for more than 50 years and it sprang from the area of languages as computers became more prevalent. NLP techniques are used in voice assistants such as Amazon’s Alexa and Apple’s Siri as well as machine translation and text filtering. NLP is essential because it helps resolve linguistic ambiguity and gives valuable mathematical structure to data for many analytical applications such as speech recognition and text analytics.
How NLP works?
Natural language processing encompasses a wide range of methods for understanding human language including statistical and machine learning techniques as well as rules-based and algorithmic approaches. Because text and voice-based data, as well as practical applications, require a diverse set of techniques. Tokenization and parsing, lemmatization, stemming, part of speech tagging, language recognition, and semantic connection identification are all basic NLP activities. NLP activities in generally break down language into smaller fundamental bits attempt to comprehend links between the components and investigate how the components interact to generate meaning. These tasks are frequently utilized in higher-level NLP skills such as Content classification that search and indexing, content warnings, and duplicate detection are all included in this linguistic-based document summary, Topic identification and modeling or properly capturing the meaning and themes in text collections as well as sophisticated text analytics such as optimization and forecasting. Contextual extraction extracts structured data from text-based sources automatically. Sentiment analysis which includes average sentiment and opinion mining identifies the mood or subjective opinions within vast quantities of text. Conversion of speech to text and text to speech. Voice instructions are converted to written text and vice versa. Document summarization generates synopses of huge volumes of text automatically. Automatic translation of text or speech from one language to another is known as machine translation. The underlying objective in all of these situations is to take raw language input and utilize linguistics and algorithms to alter or enrich the text such that it provides more value.
How do computers interpret textual data?
Text analytics which counts, classifies, and categorizes words to extract structure and meaning from enormous quantities of material goes hand in hand with natural language processing. Text analytics is a technique for analyzing textual information and generating new variables from it which may then be displayed, filtered, or fed into prediction models or other statistical approaches.
Many applications including investigative discovery combine NLP with text analytics. To assist in the detection and resolution of crimes look for trends and clues in emails or written reports. Expertise in a specific field Sort material into relevant categories so you can take action and see patterns. Analysis of social media. Track public perception and opinion on certain issues as well as important influencers.
Future with Natural Language Processing
AI will find extensive use in sectors needing natural communication as it improves its comprehension of human communication. The natural language processing capacity of AI has the potential to make a difference in a variety of domains. In reality, AI is already being applied in several of these fields.
Information Summarization uses NLP. Most prominent email service providers currently utilize AI to scan inboxes and determine the context and purpose of messages. To identify inappropriate marketing communications, spam filtering algorithms employed by email servers for example use natural language processing. This is due to AI’s capacity to understand the context and summary of the data it is given. Advanced email service companies are already experimenting with artificial intelligence that can scan messages to infer context and propose a variety of answers. However none of these systems is perfect yet and they both require continuous input, modification, and testing in order to progress and attain a dependable level of competency. Businesses may utilize AI capacity to summarize information to monitor online conversation from social media and other public platforms to obtain insights into public sentiment about their products and services. Natural language processing may also be used by government organizations to process social media chatter and assess public opinion on policy and social concerns.
We’re already witnessing an increase in the usage of emotionally intelligent chatbots in customer support. These bots are becoming more capable of not just inferring the meaning of text written in natural language but also of responding appropriately. Some bots have gotten so good at natural language processing that people on the other end have trouble telling them apart. Natural language processing will eventually allow AI-enabled virtual customer care reps to audibly communicate with human consumers and solve difficult inquiries. The bots might also be employed for technical support as they will be able to give situationally flexible assistance.
We’re already growing used to communicating with our phone’s virtual assistants which are getting better at carrying out simple functions on our phones by listening to and comprehending our voice requests. In-vehicle voice assistants that can interpret complicated requests and conduct activities to aid us with various operations will be available shortly. Smart houses with automated amenities will include smart in-home assistants to function as an interface able to interpret the orders of even small children who have yet to develop flawless speech via natural language processing. These systems will be far more intelligent than the current generation of personal voice assistants such as Alexa which has a history of misinterpreting orders. Smart home assistants will be able to not only listen to orders but also reply in a natural manner providing entertainment and help to children and the elderly.
It’s no secret that some government agencies are capable of listening in on or even seeing private conversations between members of the public despite the fact that the agencies defend their actions by invoking public safety and the necessity to detect dangerous elements. When AI can utilize natural language processing to listen in on questionable conversations without invading people’s privacy this will be less of a problem. This is similar to the email inbox scanning that many email companies do in order to obtain information on their clients.
Physicians are well recognized for spending more time filling out health record paperwork than they do consult with patients. This is an inefficient style of working that wastes time that might be used much more effectively to minimize physician mental fatigue and provide better treatment to patients. Physicians may use AI-powered by natural language processing to narrate observations and information which will be automatically entered into the EHR. Similar robots can likewise be used to replace court stenographers and accurately record court hearings.
In the not too distant future robots that can not only move but also think and communicate like humans will be commonplace. Humanoid robots that can operate like humans in every way and help people in even the most complicated activities will need to be able to understand human speech properly. Natural language processing will become more crucial than ever before thanks to the general purpose and flexible character of robots. This is because a misunderstood order might compel the robot to execute completely undesired activities potentially endangering human safety.
We will have reached the peak of human innovation and AI research if robots or programs in the future can completely interpret organically spoken or written English. In contrast to the uncomfortable reputation associated with intelligent robots, the idea of machines that can listen and comprehend us like other people makes the future of AI an intriguing prospect.
Communication is complicated by the fact that there are hundreds of languages on the globe. Language differs from one culture to the next. In terms of writing style, syntax and grammatical rules these languages are quite diverse. Similarly different accents and dialects cause significant communication disparities. NLP is still a growing idea that requires a lot of study and development to accommodate a wide range of application cases. Syntactic and semantic learning in addition to deep learning is becoming more important components of NLP. They aid in the removal of linguistic ambiguities and the improvement of NLP-based goods and services.
Author: Rashmika Prabath is a Full Stack Developer at CMS. He is a Data Science enthusiast and expanding horizons in this field.
