What is Natural Processing Language, Applications, and Challenges?

Only a few years ago it was hard to believe the fact that machines will have control over humans. As Language plays a critical role in influencing the human mind and thanks to NLP, machines have learned the tricks of the trade. Natural language processing is apparently has become akin to a central processing unit as players big and small, those who want to include it in business communications to those who are in the arms race have rigorously adopted NLP technology. Also known as computational linguistics, it is a machine-learning technique to allow machines to think and talk like humans. It actually began as a machine translation mechanism to help in deciphering code during World War II. Though the technology couldn't yield positive results, it paved the way for advanced technologies.

What is NLP

NLP as a branch of artificial intelligence is developed to help computers to understand human conversation and reciprocate the way a human can understand. NLP applications can achieve this as machine learning programs teach the system with unstructured data, which is a difficult task in itself. The tone of voice of humans, which keeps constantly changing according to popular trends, is rather impossible to mimic even after the considerable sets of training rounds. Understanding context, particularly new ones is a challenge for machine learning algorithms that are trained on a tranche of words and previous patterns the words are used. It requires semantic analysis for machine learning to get a grip on it. NLU, Natural Language Understanding is a sub-branch of NLP deals with these nuances through machine reading comprehension instead of simply understanding literal meanings of words and hence help machines understand and converse in a natural way.

NLP use cases and applications

Natural Language Processing is largely used in areas like categorizing content, extracting content, analyzing sentiment, summarizing documents, translating, deploying voice-driven interfaces, etc. If we look into the more functional applications NLP offers businesses, there is no dearth of it. From e-mail filtering to document analysis to delivering the smart functions of virtual assistants, NLP has taken over the routine of enterprises, in a good way.  If one thing businesses, in particular, can be greatly thankful to NLP, it would be sentiment analysis. NLP makes a machine capable to overhear social conversations posted in the form of comments, likes, reviews, and purchases made to extract insights. And not to talk about the chatbot, they have become the face of client management system. Now that chatGPT, the mother of all chatbots, is in the market, one can say NLP has done to AI more than what was intended.

Challenges of NLP

For sure, NLP is a powerful tool with numerous benefits, but like any other technology, it comes with its own challenges. Nothing is as diverse as human language in terms of colloquialism, slang, satire, and ambiguity in the context of the way words are used. For this Data Scientists need to teach NLP tools to look beyond definitions, word orders in order to overcome the challenge of overcoming the ambiguities and complexities pertaining to human language.