Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that empowers computers to investigate and comprehend human dialect and language. It was figured to make software that produces and recognize normal language, so a client can have common discussions with his or her PC rather than through programming or artificial languages like Java or C.
HOW NLP WORKS ?
Processing the spoken or written word relies heavily on Big Data, large amounts of structured, semi-structured, and unstructured data that can be mined for information. Computers can quickly go through the data, analyze it, and find patterns or trends. Initially, NLP relied on basic rules where machines using algorithms were told what words and phrases to look for in text and then taught specific responses when the phrases appeared. It has evolved into deep learning, a flexible, more instinctive method in which algorithms are used to teach a machine to identify a speaker’s intent.
In the evolution of NLP, algorithms have been historically bad at interpreting. However, now with improvements in deep learning and AI, algorithms can now successfully interpret. Today’s researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services.
APPLICATIONS OF NLP:
Brings Efficiencies in Voice Recognition
Voice recognition technology has greatly evolved with the arrival of NLP in artificial intelligence. Many of us have seen this new technology applied to voice commands spoken to mobile devices for finding locations, getting directions or updating calendars. Customers are amazed when a system has the ability to have a conversational interaction that was never possible with traditional touchtone or speech voice recognition. Today’s artificial intelligence can handle complex conversations in a free flow form that is similar to natural speech.
This technology assists customers calling into reservations desks, accounting offices, asking for service assistance of other business and consumer situations without the need to engage a live agent. Virtual agents support the routine parts of a call and seamlessly transfer the caller to a live agent along with the information already collected. Transaction types that typically lend themselves to virtual agent technology is order status, order taking, order changing, bill payments, inquiries, account changes, scheduling appointments etc.
Vendors claim that by using virtual agents will realize cost per call decrease as much as 65%, headcount is reduced by as much as 18%, self-service completion increase by up to 35%, average handle time (AHT) is reduced by as much as 50% and caller abandonment rate increases by as much as 17. It is impossible to verify all claims, but it is clear that industry experts agree that virtual agents are here to stay. It is aimed that by 2020, the customer will manage 85% of the relationship with an enterprise without interacting with human.
Effective in Human Cure
Medicine NLP is applied in medicine field as well. The Linguistic String Project-Medical Language Processor (LSP-MLP) is one the large scale projects of NLP in the field of medicine .The LSP-MLP helps enabling physicians to extract and summarize information of any signs or symptoms, drug dosage and response data with aim of identifying possible side effects of any medicine while highlighting or flagging data items . The National Library of Medicine is developing the Specialist System, it is expected to function as Information Extraction tool for Biomedical Knowledge Bases, particularly Medline abstracts. The Columbia University of New York has developed an NLP system called MEDLEE (Medical Language Extraction and Encoding System) that identifies clinical information in narrative reports and transforms the textual information into structured representation.
Dialogue System a new approach
Perhaps the most desirable application of the future, in the systems envisioned by large providers of end user applications, Dialogue systems, which focuses on narrowly defined applications (like refrigerator or home theater systems) currently, uses the phonetic and lexical levels of language. It is believed that these dialogue systems when utilizing all levels of language processing offer potential for fully automated dialog systems. Whether on text or via voice. This could lead to produce systems that can enable robots to interact with humans in natural languages. Examples like Google’s assistant, Windows Cortana, Apple’s Siri and Amazon’s Alexa are the software and devices that follow Dialogue systems.
Internet, Web and Digital Library Application
According to a recent Survey, 55% of the Internet users are non-English speakers and this is increasing rapidly, thereby reducing the percentage of net users who are native English speakers. However, about 80% of the Internet and digital library resources available today are in English. This calls for the urgent need for the establishment of multilingual information systems. At the user interface level, there has to be a query translation system that should translate the query from the user’s native 18 language to the language of the system. Several approaches have been proposed for query translation. The dictionary based approach uses a bilingual dictionary to convert terms from the source language to the target language. Intelligent News Filtering Organizational System (INFOS) that is designed to filter out unwanted news items from a Usenet. INFOS builds a profile of user interests based on the user feedback. After the user browses each article, INFOS asks the user to rate the article, and uses this as a criterion for selection (or rejection) of similar articles next time round.
Proficiency in languages was traditionally a hallmark of a learned person. Although the social standing of this human skill has declined in the modern age of science and machines, translation between human languages remains crucially important, and is perhaps the most substantial way in which computers could aid human-human communication. Moreover, the ability of computers to translate between human languages remains a consummate test of machine intelligence: Correct translation requires not only the ability to analyze and generate sentences in human languages but also a humanlike understanding of world knowledge and context, despite the ambiguities of languages.
Enthusiastic researchers have had high hopes that the language-understanding ability of robots in science fiction movies was just around the corner. However, in reality, speech and language understanding did not work well enough at that time to power mainstream applications. The situation has been changing dramatically over the past five years. Huge improvements in speech recognition have made talking to your phone a commonplace activity, especially for young people. Computer systems trade stocks and futures automatically, based on the sentiment of reports about companies based on the sentiment of report about companies. As a result, there is now great commercial interest in the development of human language technology, especially because natural language represents such a natural interface when interacting with mobile phones.