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All You Need To Know About Natural Language Processing (NLP)

 July 14  | 0 Comments

Technology and its innovations have become indispensable to our lifestyle in the digital age. In a digitally dominated world, an enormous amount of data gets generated every second at an alarming rate. The surge of Big Data and Data Science has made it essential to use raw data to create several data-driven decisions. The question remains, how in the world are we going to handle all this information. The answer is through Natural Language Processing (NLP).

What Is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is the ability of computers to understand human speech or text in spoken or written formats. It makes it possible for computers to decipher human interactions in a useful manner. It is a meeting point of sorts for computer science, artificial intelligence, and computational linguistics. It’s extensively used for text mining, machine translation, and automated question answering.

What Are The Applications Of Natural Language Processing (NLP)?

Developers use NLP algorithms to summarize and extract relevant information from large chunks of raw data primarily. It is being increasingly used for the following:

  1. Text Classification
  2. Language Modeling
  3. Speech Recognition
  4. Caption Generation
  5. Machine Translation
  • Text Classification

Wherein text inputs can be categorized according to theme or tonality etc. This application is seen to aid in filtering spam emails, language identification, sentiment analysis (of product reviews) and genre classification (of books, movies, arts etc).

  • Language Modeling

Here the computers use NLP to predict a suitable headline or phrase based on the initial source of text. It can be seen in generating new headlines, new sentences, paragraphs, or documents as well as for predicting the suggested continuation of a sentence.

  • Speech Recognition

Here NLP is used to create the right text output based on audio input data received. Transcribing a speech, subtitle addition and even virtual personal assistants like Siri work on this capability.

  • Caption Generation

Through this, a digital image/video can be described in text form. It’s not only useful for those who are visually impaired but also for generating search results of images & videos with regards to input text.

  • Machine Translation

Here, the input data is translated from the original language to another based on user demands. These include translating a text document or audio file from any foreign language to English or vice versa.

If the above applications left you intrigued, check out this blog on chatbots and other virtual assistants that work on NLP!

What Are The Skills Required To Become An NLP Expert?

The ideal candidates for job roles in Natural Language Processing (NLP) should have command over both linguistics and computers. You need to understand the aspects and concepts of linguistics like speech recognition, information extraction, sentence fragmentation, parts of speech, and so on.

As far as your programming is concerned, you are expected to be well-versed in at least one language like Python or Java or Ruby. Also, you should know the basics of machine learning, probability, statistics, recursive neural networking (RNN) and NLP.

What Are The Career Opportunities In Natural Language Processing (NLP)?

The opportunities in NLP include roles like NLP engineer, NLP scientist, NLP architect, Voice Over Artist, NLP applied research scientist, cognitive data scientist, etc.

The average salary of a Machine Learning NLP engineer in the US is within $119,256 – $169,853 per year while an NLP Research Scientist would earn around $72,040 per year.

Over the past 2 years, Natural Language Processing (NLP) has featured among the top jobs on major job search platforms. Professionals and students with foresight have been learning the latest technologies like Big Data, Data Science, and Analytics for this reason. If this has spiked your interest, you should definitely check out this link.

Happy Learning!