What is Natural Language Processing (NLP)?
Continuing with my readings of ChatGPT and Google Colab: The Easiest, Quickest Way to Start Learning AI by Dr. Kelsey, I finally made it to the chapter on Natural Language Processing or NLP. I have heard of NLP in passing through my Machine Learning classes and by exploring more ML topics with ChatGPT. Dr. Kelsey describes NLP as "a field of artificial intelligence (AI) that focuses on helping computers understand, interpret, and generate human language." [1]
I asked ChapGPT to define NLP, and this is what it said:
"Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand, interpret, and respond to human language in a valuable way."
What is an example of an NLP program? Well, ChatGPT, of course! But NPLs go beyond creating text. Another application of NLP is text analysis and speech recognition. If you have an iPhone, you have an NLP in your back pocket: Siri. Alexa is another example of an NLP. These text-to-speech (TTS) voices are perfect examples of NLPs because they (1) understand human language, (2) adapt to new linguistic patterns, (3) and provide accurate responses.
What other uses does NLP have? Another NLP program I've used plenty in the past is Grammarly. Yes, Grammarly spots grammar mistakes but also considers your tone and intentions and provides suggestions for improvement. Grammarly falls under the Sentiment Analysis application of NLPs.
There are three other common applications for NLP programming: Language Translation and Information Extraction. If you've ever used Google Translate, then you've used an NLP application. If you are bilingual and try to translate common phrases or saying, you'll notice there are some errors here and there, but that's all part of the ML process. You can do your part by providing feedback to Google and help improve the algorithm!
I was less familiar with Information Extraction NLP, but after asking ChatGPT for a list of common applications, I realized that this might be the most widely used aspect of NLPs. Here is the list ChatGPT provided me:
- Search Engines like Google and Bing
- Customer Relationship Management (CRM) Systems like Salesforce
- Business Intelligence Tools like Microsoft Power BI
- Content Management Systems like WordPress
- Legal Document Analysis
- Academic Research Tools
- Healthcare Data Analysis Systems
- Social Media Monitoring Tools like Hootsuite and Sprout Social
- Recruitment Software like LinkedIn Recruiter
Clearly, NLP is everywhere today and we've grown accustomed to its convenience. However, the same challenges stand: slang, idioms, regional dialects, and varying syntax prove to continue being a barrier to perfectly clear NLP processing. I am excited to see how NLPs continue to get better as large-scale language models emerge.
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