Here is a breakdown of all things natural language processing as well as some examples of learning paths to get started.
Natural language processing enables computers to process human speech patterns and ‘read’ text. It is a branch of computer science that encompasses AI and linguistics.
It might sound rather futuristic, but most of us use the tech every day. Things like spell check, voice text messaging, autofill and even making use of the related words feature on search engines.
Those are all examples of tech that has been facilitated by natural language processing. Another famous example is Alexa/Siri/Google Assistant.
With natural language processing, computers can be programmed to ‘learn’ a lot more than humans can. It is definitely an evolving area of tech, and it is being leveraged by a lot of organisations in interesting ways. In January, SiliconRepublic.com predicted the tech would be a major AI trend this year.
If you have a good grasp of natural language processing, you’ll be an asset to any company – particularly those that deal with a lot of customers.
Techniques and uses
Lots of customer service sites have it integrated into their platforms. The tech powers virtual assistants, chatbots and messenger bots on things like taxi or ride sharing services.
There are several different types of natural language processing techniques. Here is a quick breakdown of them:
Sentiment analysis: automated process of labelling messages as positive, negative or neutral.
Named entity recognition: finding and classifying essential information or a word or phrase.
Summarisation: use of deep learning and machine learning models to summarise long texts.
Topic modelling: unsupervised machine learning method that identifies topics that occur in a collection of texts.
Text classification: categorising text into distinct groups.
Keyword extraction: taking the most used words from a piece of text as a way of summarising it.
Lemmatisation and stemming: text normalisation techniques.
Now that you know a little bit about natural language processing and what its uses are, let’s take a look at some of the online courses out there that can help you upskill.
Udemy has an Introduction to Natural Language Processing course that takes a manageable three hours to complete. It takes you through basic concepts and only requires basic Python programming knowledge.
It’s a language that is used for a lot of natural language processing tasks so it’s a good idea to brush up on Python if you haven’t already got at least a basic grasp of it.
If you want to take a course that introduces you to natural language processing with Python, then Pluralsight has a good course offering just that.
For those interested in natural language processing’s role in AI and machine learning, this course by FutureLearn might be a good fit. It is accredited by Microsoft.
Finally, Udacity has a longer course that lasts around three months for learners who are ready to progress to a more advanced stage. Enrollers should have Python experience as well as machine learning and statistics knowledge.
Machine learning is definitely another subset of AI that if you familiarise yourself with it, your understanding of natural language processing techniques will also benefit.
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