Computers are great at processing and computing standard structured data like tables, excels and financial records (even gibberish in 0 and 1s). They are able to process that data much faster than humans can do manually. But us humans don’t share information in the form of tables or excels with each other nor do we speak binary! We communicate using proper words and sentences.
Unfortunately, computers working with unstructured data like words and sentences is a well-known computing problem in data processing because there’s no standardized techniques or rules for processing. When we program computers using some High level language like C++, Java, or Python, we are essentially giving the computer a set of rules that it should operate with. With unstructured data like paragraphs and texts, these rules are quite abstract and challenging to define concretely.
Human vs Computer understanding of language
Humans have been writing down notes, literature, science for thousands of years. Over that time, our brain has gained a tremendous amount of experience in understanding natural language. When we read something written on a piece of paper or Internet websites, we understand what that thing really means in the real-world. We feel the emotions behind the thing mentioned in content and we often mentally visualise how that thing(s) would look in real life.
Natural Language Processing (NLP) is a defined sub-field of Artificial Intelligence (AI) that is focused on enabling computers and supercomputers to understand and process human languages, to get computers to a human-level understanding of language. Computers don’t possess an intuitive understanding of natural languages like humans do. They can’t really understand what the language is really trying to express. In a nutshell, a computer can’t read between the lines.
But recent advances in Machine Learning (ML) have enabled computers to dig quite a lot of useful things with natural languages! Deep Learning has enabled us to write programs to perform things like language translation, semantic understanding and corrections, and text summarization. All of these things add real-world value to business, making it easy for programmers and data scientists to understand and perform computations on large blocks of text without much manual effort.
Natural Language Processing!! Who uses it??
Most of the researches being done on natural language processing or speech processing revolves around search, especially enterprise search. This involves allowing users to query data sets in the form of a question in english or any established query language that they might pose to another person or entity.The machine interprets the important elements of the human language sentence, such as those that might correspond to specific features in a data set, and returns a suitable response.
NLP can be used to interpret free text and analyse it for business insights and review collection etc. There is a huge heaps of information stored in free text files, like patients’ medical records , customer reviews which were not accessible to computer assisted analysis before deep-learning based nlp models came into scope. NLP allows analysts to sift through massive troves of free text to find relevant information in the files.
Sentiment analysis is another primary use case for NLP. Using sentiment analysis, data scientists can assess comments on social media/feedback forms to see how their business’s products are performing, for example, or review notes from customer service teams to identify areas where people want the business to improve.
Google, Yahoo and other search engines use their machine translation technology on NLP deep learning models like tensorflow. This allows algorithms to read plain text on webpages, understand its linguistic meaning and translate it to another language.
Natural Language Processing!!How this works?
Current approaches to NLP are based on deep learning or neural networks, a type of AI that examines and uses patterns in data to improve a program’s interpretations. Deep learning models require massive amounts of classified or labelled data to train and identify relevant correlations, and assembling this kind of big data set is one of the main challenges to NLP currently.
Earlier approaches to NLP involved a more rules-based general approaches, where simpler machine learning algorithms were trained about what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible and intuitive approach in which algorithms learn to identify user’s intent from many examples, almost like how a child would learn human language by continous interpretation.
Machines' Guy 