Hello, language lovers! Welcome to Week 3 of our CSOC journey. In the previous weeks, we've already dived into the fascinating world of Machine Learning and Neural Networks. Now, it's time to add another feather to our AI cap - Natural Language Processing (NLP).
NLP is a subfield of AI that focuses on the interaction between humans and computers using natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way. It's like teaching a new language to a baby, but this time, the baby is an AI model.
In the era of Siri, Alexa, and Google Assistant, the importance of NLP is self-evident. It's the driving force behind these voice-driven AI systems. From spam filters and voice text messaging to customer service bots, NLP is everywhere. It's the unsung hero making our interactions with machines smoother and more natural.
Machine Learning is the backbone of NLP. It allows the system to learn from data (text) without being explicitly programmed. It's like the 'brain' of our NLP 'baby'. It learns the language, understands the context, and even gets the humor (sometimes!).
Before we dive into the deep end of NLP, we need to understand text preprocessing. It's like learning the alphabet before writing essays. Text preprocessing involves cleaning and formatting the data. This step includes techniques like tokenization, stemming, and lemmatization.
Don't worry if these terms sound like a foreign language. We'll decode them together. For now, just remember that text preprocessing is the first and crucial step in any NLP project. This is a great resource to get started with it:
Now that we've preprocessed our text, it's time to introduce it to our 'brain' - the Recurrent Neural Networks (RNNs). RNNs are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or spoken words.
Unlike traditional neural networks, RNNs have a 'memory' that captures information about what has been calculated so far. In other words, they are 'recurrent' in their ability to take information from the past to understand the present. It's like remembering the previous words in a sentence to understand the current word.
For a more detailed understanding of RNNs, check out this link:
An Introduction to Recurrent Neural Networks for Beginners - victorzhou.com
While RNNs are great, they do have their limitations. They suffer from something called the 'vanishing gradient problem', which makes it hard for them to learn and understand long sequences (like long sentences or paragraphs).