Recent approaches to NLP are based on machine learning, where data is fed into the learning machine with every action and non-action feeds and then the task gets automated without constantly requiring human or manual interference. Machine learning has allowed computers to find hidden insights, using algorithms that repeatedly acquire from data that is provided to them, without being programmed explicitly where to look. Most of the research being done on natural language processing revolves around search, especially enterprise search. It is the ability of a computer program to understand human speech as it is spoken.

NLP often referred to as computational linguistics has some tasks involved like named entity extraction, deep analytics, sentence segmentation, co-reference resolution, part-of-speech tagging, and parsing. It is the field that focuses on the interactions between computers and human language. It sits at the intersection of artificial intelligence, computational linguistics, and computer science. For any search using natural language processing, the program will itself recognize any abbreviated term or acronyms. The challenge lies where computers have to understand the way humans use and learn the language. Human language is neither too simple nor is it precise. For computers to understand human language, they need to understand the concepts and their linkage with the words to create a meaningful sentence. For example, consider a statement “Baby swallows fly.” Now, this could have various meanings making it tough for the program to understand the exact meaning behind it. Like the word fly or swallow is used as a verb, that makes the baby as an adjective or a noun. In the course of human communication, the meaning of the sentences depend on both the understanding of each person’s ambiguity in human languages and the context in which it was communicated. This is where the software has to be programmed to understand linguistic and context structures.

AI is going to be the newest way we interact with all our gadgets; our cars, fridges, smartphones, front door and central heating system. We would be dwelling in an always-on world. Making our gadgets to predict our intentions and comprehend our current context is the formula for building extremely brainy products. A billion connected ‘things’ are now actively requesting support from AI platforms.