A basic understanding of Natural Language processing for beginners

A basic understanding of Natural Language processing for beginners

Natural Language Processing for Beginners has been on the scientific charts for a long period of time. Scientists and engineers have been at it for decades. Now, with factors like big data, machine learning, and deep neural networks chipping in, things have changed very quickly. NLP has jumped out of the scientific closet and dived nose-first into the thick of things.

We will not go into the math’s of it; we will not explain how the matrices work; those things are for you to learn from machine learning courses in Delhi. Yes, if you are in India and willing to learn machine learning with a specialization in natural language and processing, then the machine learning institutes from Delhi and Bangalore are your best bet. Anyway, we will try to grasp the basic ideas behind Natural Language Processing for Beginners and its importance in today’s world.

Why is NLP so important?

Before we dive into the details, let’s take a quick look at why NLP is so important. The provision of translation services has long been acknowledged as a highly-skilled undertaking. It’s also a time-intensive one, with qualified linguists pouring hours into finding the right phrasing in order to deliver accurate translations.

The advance of natural language processing has provided the scope to reduce both the time that translation takes and how much it costs. In the business world, where time is money, this is a seriously big win. So, where did NLP spring from?

Let us start with some history

The earliest ancestor of natural language processing was Machine Translation or MT. This referred to the use of computing machines to translate one language into another. This could have been done through the manual association of an English word with its Russian counterpart – yes, those are the two languages that were used initially. This was in the 1940s during the Second World War.

Skip a couple of decades and you have a nifty little chatbot called ELIZA. It was the creation of Joseph Weizenbaum who used a script called DOCTOR. It could answer psychometric-based questions from users. It was designed to be therapeutic for psychiatric patients. It was the first chatbot that took the Turing test – a test designed to determine how humane a machine is.

The research on NLP had a hiatus until the 1980s after that. Now, we are in 2020 and the field of NLP has grown leaps and bounds and not without hitting some hurdles.

The two parts of it

Natural language processing has two aspects to it. One is natural language understanding or NLU and the other part is natural language generation or NLG. Together they help the machines understand and generate language as we understand it.

Natural Language Understanding

Machines can handle structured data quite easily but when it comes to unstructured data, pieces of texts in this case, the machine has first to internally draw some structures out of it. NLU is the process through which a machine can break language down to rules and symbols and then create a structure with those which it can interpret.

Why is it useful?

NLU can help automate a lot of tasks which would be difficult and in some cases impossible to achieve otherwise. For example, natural language understanding can help a machine detect profanities in a piece of text. Well, the machine does not understand what the profanity means nor does it have feelings to be hurt, but it knows that the structure of signs and symbols under consideration here is something that has to be eliminated.

Similarly, it can detect the intent of a sentence, classify topics, recognize an entity mentioned in a piece of text, and whatnot. With the help of deep learning, NLU can not only deconstruct a query made in the natural language it can also tap data about similar queries made previously and the ideal responses.

Natural Language Generation

This is what completes Natural Language Processing and bridges the communication gap between machines and human beings.

It takes facts that the machines can understand and turns it into language that we, humans can understand. It uses mathematical formulae to extract information from datasets and translates it into text.

An example

Automated journalism is a classic example of NLG. AI is implemented to scrape data from various sources of news across the web and then create a short summary of the news which is ready to be published. This would take human being hours of hard work only for limited and nonexhaustive results.

The importance of NLP

Let us take the example of an energy company. It can have oil rigs in very harsh climates. Now the heavy machinery used in these plants needs maintenance, and every time a person is perched a hundred feet above the ground to fix a machine, it is a potentially fatal hazard. There is plenty of data regarding injury reports and repair manuals, but most of it is in unstructured, textual form hence, largely inaccessible when needed. NLP can be used to interpret textual data and generate life-saving insights. The same goes for any industry which wishes to make good use of its data.

 

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