Blog: A sneak peek into Natural Language Processing
Natural Language Processing (NLP) is primarily a sub-branch of Artificial Intelligence which focuses on equipping the machines to understand and process human languages and bring them closer to human understanding of language.
Natural Language processing is one of the highly researched areas in today’s world as it develops the thinking ability of machines using human language, which is one of the most versatile features of human beings. Latest techniques like deep learning have transformed the NLP industry by making language translation, speech to text recognition etc. much easier than before.
Use cases for NLP
As technology revolution becomes one of the most talked about things today, we are entering a voice-first world with the advent of chatbots like Google Home, Amazon Echo etc. Voice enabled platforms have the characteristic feature that both their input and output is voice. The inputs are processed using Natural Language Processing wherein the machine is programmed to understand the user’s intents and respond accordingly which then gets converted to an output.
Voice-first technology is an exciting space to work in and there is a lot of scope to build solutions that can cater to the masses.
Sentiment Analysis was initially used in Text Analysis where text is analyzed to predict whether the sentiment of the text is positive or negative. It has now become a powerful tool for customer engagement as sentiment analysis can tap how customer’s feel towards a particular brand. NLP helps you develop actionable strategies for your brand development and growth.
Machine Translation is the translation from one language to another by keeping the integrity of the input text and producing output text that’s as close to the original as possible. Although language translation is a much older research subject, due to the recent advancement in natural language processing research methodologies, translation has become a highly advanced research topic with more precision in the translation results due to deep linguistic analysis and better statistical models.
An interesting research topic in the field of NLP revolves around the topic of enterprise search. Old keyword-based search mechanisms have been replaced by cognitive search which used Natural Language Processing in the background. The search results are not just based on answering simple SQL queries but they organise the given input data and predict the intent with which the search query was asked. All the chatbot devices are powered by strong search engines behind them and as cognitive search becomes more and more efficient, the voice-first agents are becoming much better communicators.
Hurdles in NLP
Several deep learning approaches to NLP are being used to understand data patterns to improve user understanding. But one of the biggest hurdles that Natural Language Processing faces is that human language is not made up of definitive rules, it’s ambiguous.
In order to understand human intent, there is a requirement of not only abundant data but the attachment of context with every data set that’s available. The rules created for machines may/may not be the same when it comes to language construction, therefore being able to understand both the words and the concepts is what can deliver an effective message/processing.
How can you get started?
Due to the popularity of Natural Language Processing, the subject has gained a lot of momentum across the globe. There are various web tutorials, online coding materials (preferably Kaggle) that cover a vast range of topic under Natural Language Processing.
But the best way to start learning is to register yourself in a course which is not only comprehensive but offers hands-on insights into how you can apply NLP to a real-world problem.
For more courses and further information, you can check out GitAcademy.