Machine Learning is one of the wonders of modern technology! Intelligent robots, smart cars etc. are all applications of ML. And the technology that can make robots talk is called Natural Language Processing!!!
This article focuses on the applications of Natural Language Processing and emphasizes the vast scope of this field. To understand the basics of N.L.P from a technical point of view, click here. Otherwise, let’s move on to the basics of Natural Language Processing and its various applications.
While Natural Language Processing is a spectacularly interesting field with infinite potential and a multitude of applications, it is a relatively unexplored field (when compared to, say, image processing.) due to the fact that getting computers to understand text is a much bigger challenge than getting it to understand numbers. More importantly, language is not like Maths. Languages are messy and ambiguous. Even the tasks the smartest computer can perform today, are carried out by reducing the most complex of logic to a sequence of zeroes and ones. The irony is that an ocean of application based opportunities is on the other side of overcoming the challenge of moving beyond zeroes and ones.
When it comes to converting text to numbers, one might intuitively think of using ASCII values. Though that seems like a good idea (and the obvious) answer at first, consider this: The main purpose of a language is to be able to communicate with meaning. Converting text to its numeric form will completely get rid of its contextual and semantic meaning. Thus, enter- word vectors. The term is self explanatory, but the beauty of converting words to vectors is that vectors invite mathematical operations upon themselves. So not only do we end up converting text to its numeric form without a loss of context/meaning, but we also ensure that context will remain intact on the future execution of multiple mathematical operations and functions. This is the fundamental step in moving beyond zeroes and ones.
Applications of Natural Language Processing
So, what are the applications of Natural Language Processing? Some major applications have been mentioned in the article (link above) “An Introduction to NLP.” However, let’s take a closer look at the problems that have been solved using Natural Language Processing:
1. Healthcare-Dragon Medical One:
A healthcare solution by Nuance, Dragon Medical One is capable of allowing doctors to dictate basic medical history, progress notes and even future plans of action directly into their EHR.
2. Computerized Personal Assistants and Personal Virtual Assistance:
Do we have what it takes to take Siri/Alexa one step further? It is a known fact that one of NLP’s largest application in the modern era has been in the design of personal voice assistants like Siri, Cortana and Alexa. But imagine being able to tell Siri to set up a meeting with your boss. Imagine if then, Siri was capable of somehow comparing your schedule to that of your boss, being able to find a convenient time for your meeting and then revert back to you and your boss with a meeting all fixed. This is what is called a Personal Virtual Assistant.
3. Customer Service:
Using advanced concepts of Natural Language Processing, it might be possible to completely automate the process of handling customers that call into call centers. Not only this, it might become easier to retrieve data from an unorganized structure for said customers using such a solution.
4. Sentiment Analysis:
Already a booming talk point in social media analytics, NLP has been used extensively to determine the “sentiment” behind the tweets/posts of users that take to the internet to share their emotions. Not only that, it may be possible to use Sentiment Analysis to detect depression and suicidal tendencies.
Thus, Natural Language Processing is a concept in its infancy with infinite potential. How well we learn it and how well we use it is completely up to us!
- Translation and Natural Language Processing using Google Cloud
- Difference between Text Mining and Natural Language Processing
- Introduction to Natural Language Processing
- ML | Natural Language Processing using Deep Learning
- Syntax Tree - Natural Language Processing
- Analysis required in Natural Language Generation (NLG) and Understanding (NLU)
- Processing text using NLP | Basics
- ML | Understanding Data Processing
- Understanding Tensor Processing Units
- Processing of Raw Data to Tidy Data in R
- How to use Google Colaboratory for Video Processing
- NLP | Parallel list processing with execnet
- Digital Image Processing Chain
- CNN - Image data pre-processing with generators
- Project Idea | Audio to Sign Language Translator
- Why is Python the Best-Suited Programming Language for Machine Learning?
- Interquartile Range and Quartile Deviation using NumPy and SciPy
- Supervised and Unsupervised learning
- Underfitting and Overfitting in Machine Learning
- Regression and Classification | Supervised Machine Learning
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