Natural Language Processing: The future of text analysis
Apart from Machine Learning, Natural Language - in all of its indications, including Natural Language Understanding (NLU), Natural Language Processing (NLP), Natural Language Generation (NLG) and Natural Language Interaction (NLI) are likely the most noticeable kind of Artificial Intelligence in presence.
Artificial Intelligence text analysis is the method of obtaining knowledge from huge volumes of text data. For a program, to understand this data and make sense of it, Natural Language Processing plays an important role. This technology can help businesses to automate the process of gathering and understanding the consumer feedback on a wide scale so that decisions to improve the business can be made based on data.
The amount and type of data generated over various channels rise at an exponential speed, giving businesses with a large amount of data about their consumers. In order to enhance the experience of the customers and the processes involved in the business, companies are switching to text analysis to leverage the unorganized text contained in social media posts, open-ended surveys, contact center records, and other sources of feedback data.
Traditionally, Natural Language Processing has performed a prominent part in promoting text analytics, for which interest is not likely to diminish any time soon. Although the practical use for this application will proceed to increase in the coming years, the whole natural language series of technologies are also offering notable benefits in a progressive speech to text and semantic search use cases as well.
Reasonably the most prominent result of these trends is that in many cases, natural language's ability is considerably enhanced by the utilization of Machine Learning. And at the same time, these technologies will make Artificial Intelligence more acceptable and accessible to users who are not fully aware of the impacts these can cause on the world.
The role of Natural Language in Artificial Intelligence is mainly based on the functions of various technologies.
Natural Language Processing:
Natural Language Processing, or NLP in short, is broadly described as the automated use of natural language, such as text and speech, by software. The research of Natural Language Processing has been around for more than 40 years and rose out of the area of linguistics with the emergence of computers. It is a way for machines to interpret, learn, and acquire meaning from human language in an intelligent and beneficial manner. By using Natural Language Processing, developers can structure and organize information to execute tasks such as translation, sentiment analysis, speech recognition, automated summarization, named entity identification, topic segmentation, and relation extraction.
Natural Language Understanding:
Natural Language Understanding (NLU) is a part of Natural Language Processing (NLP), which assists machines in understanding and interpreting human language by dividing the elemental bits of language. While speech recognition captivates verbal speech in actual time, copies it, and delivers text, NLU operates beyond identification to learn a person's intention. Speech recognition is powered by Analytical Machine Learning techniques that supplement numeric structures to huge datasets. In Natural Language Understanding, Machine Learning patterns develop over time as they learn to identify syntax, circumstances, language patterns, unique descriptions, emotion, and purpose.
Natural Language Generation:
Natural Language Generation (NLG) is the application of Artificial Intelligence (AI) programming to which results in spoken or written narrative from a dataset. It is similar to natural language understanding, natural language processing, and computational linguistics, the field of Artificial Intelligence involved with human-to-machine and machine-to-human communication. NLG studies usually concentrate on developing computer applications that produce data results with meaning. Advanced NLG software has the intelligence to dig vast volumes of statistical data, recognize patterns and share that data in a form that is simple for humans to understand. The pace of this software is very helpful for generating news and other time-sensitive write-ups on the internet.
Natural Language Interaction:
Natural Language Interaction (NLI) is the combination of various sets of Natural Language principles that facilitate humans to communicate with any associated devices or services in a humanlike manner. Frequently acknowledged as conversational AI, Natural Language Interaction enables technology to interpret complicated sentences, comprising various bits of information and more than 1 request. It can then respond accordingly, generating value and improving the experience of the user. At present, Natural Language Processing is most widely adopted in machine interpretation and cognitive research. In recent years, conversational research, such as when one requests Alexa to shuffle songs in a playlist or ask Siri to look up the weather in New York, is responsible for the exciting rush in human-machine communication. Chatbots are an excellent instance of how NLP is being utilized today. With the application of NLP, chatbots are capable of managing complicated communications and organizing business processes.
Natural Language Processing is also beneficial for businesses to interpret public opinion regarding their organizations. Sentiment analysis is the process of recognizing human sentiments obtained from a text in a social media post. With NLP, businesses are now capable of accessing public opinion about their brand quicker and more efficient than earlier, and can instantly decide whether they are positive feedback or unwanted review and approach their client matters in a timely fashion.
NLP is also discovering applications in different spectrums of influential enterprises. Banks and many other economic servicing companies already use virtual assistants to solve primary client queries like how to open an account or suggestions for deciding among kinds of accounts. Main automotive companies have combined voice identification, Natural Language Understanding (NLU) and text-to-speech answers in their operator support methods for access applications and assistance through speech commands. Likewise, the healthcare industry is combining NLP into its doctor documentation, enabling for the prompt documentation of a patient's account in real-time. The use of Natural Language Processing is anticipated to pick up force in the upcoming years with the use of added functionalities in smartphones, more personal assistants like Alexa, Cortana, Siri, etc, and the growth of Big Data to automate even more conventional tasks of humans. The need for this technology is pushed by various fundamental factors which also include the ever-rising knowledge of production in industries across the world, the increasing need for excellent client experience, and the enhanced utilization of smart gadgets over companies.
Here's how you can use Natural Language Processing in your business: Natural Language Processing has many applications in various areas such as education, sales, business, sports, and therapy. Companies are switching to Natural Language Processing (NLP) technology to infer knowledge from the infinite volume of unstructured data obtainable online. Here's how NLP can be applied in businesses:
Email Filters: One of the most commonly used applications of Natural Language Processing is email filters. By interpreting the text in the emails that pass within the servers, email providers can prevent spam emails from invading their mailboxes.
Data Extraction: Several significant decisions in companies are progressively driving apart from human control. The majority of the information is in the form of images, text, or infographics. NLP obtains and extracts knowledge from these forms of information which leads to effective decision-making abilities.
Voice Recognition: Companies are using Natural Language Processing technology for understanding the queries of humans.
Sentiment Analysis: Sentiment analysis is broadly applied in monitoring the web and social media because it enables companies to achieve a widespread public impression on business and its services. Hearing to customer's voice needs a profound perception of what they express. To crack the sentiment behind the human language, NLP plays an important role.
Customer Service: Chatbots can combine to the customer service abilities of business by responding to regular queries and managing simple requests.
The future of Natural Language Processing:
As technology advances, NLP will remain developing in blending with Machine Learning. For example, modern, more intelligent chatbots are starting to utilize deep learning to investigate human response data and produce a reply respectively. What does this imply? Well, for music enthusiasts it happens that music systems will learn to play tunes depending on the most recent playlist of an individual. Natural Language Processing is also presumed to contribute more convenience to people when linked to the applications of IoT (Internet of Things), such as smart homes, which enable individuals to control smart devices with the help of their voice. The real solution to opening the prospect of NLP is Natural Language Generation (NLG), which will allow computers to comprehend huge amounts of statistical data, recognize patterns and share that data in a way that is understandable to people. The application of NLG and similar Artificial Intelligence tools will let companies handle and use massive amounts of data efficiently. Until lately, media organizations tried excessively by creating content that the public would find appropriate, engaging or informational. However, with the use of NLG, many such organizations are starting to change data about multiple events into intelligent narratives. This method can save the time of writers and lets them concentrate on other significant tasks. Beyond the media industry, NLG is expected to be employed in insurance, government, banking, healthcare, retail, and financial services for purposes such as scam exposure, predictive support, uncertainty and compliance administration, and client experience supervision. In fact, in two to three years, it is predicted that the Natural Language Generation will be an essential aspect of Business Intelligence. Modern advancements in Artificial Intelligence and NLP are actively transforming interactions between machines and humans, and even further conventional human tasks are assumed to be computerized in the upcoming years. Motivated by the exponential increase in data, the heightened focus on excellent client encounters, and the generation of smart appliances, NLP has become an actuality of everyday life and has been demonstrated to be one of the most notable technological advances in all enterprises in recent years.
Author Bio: Maria Thomas is a Content Manager at GreyCampus. She has over 4+ years of experience in the industry and has a keen interest in writing about the ever-changing world of emerging technologies and upcoming trends. She has excellent experience in developing content for professional certification courses such as AIML, NLP , PMP- Professional Project Management, IoT, AWS, Python, Ruby, and Six Sigma.