Natural Language Processing, or NLP, is made up of Natural Language Understanding and Natural Language Generation. NLU helps the machine understand the intent of the sentence or phrase using profanity filtering, sentiment detection, topic classification, entity detection, and more.
In this episode, Tia breaks down the differences between NLP, NLU, and NLG, and explains how Deep Learning plays a role in getting you better search results, faster.
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(speaking into her apple watch) Hey Siri, what’s the weather like today?
We are truly living in the future, you guys.
Natural language processing!
It’s how I can talk to my watch, my phone, or my smart speaker – and how all of those things can talk right back. Let’s break it down. We’ve got NLP, NLU, and NLG.
Often people will use these terms interchangeably, but that’s not quite right. In reality, Natural Language Processing is made up of Natural Language Understanding and Natural Language Generation.
It all starts when NLP turns unstructured data into structured data to be analyzed with NLU.
NLU helps the machine understand the intent of the sentence or phrase and is used in:
- Profanity filtering
- Sentiment detection
- Topic classification
- Entity detection
- And more
NLU is used along with search technology to better answer our most burning questions.
In traditional Natural Language techniques, the question is pulled into a graph structure that deconstructs the sentence the way you did in elementary school.
Newer techniques use Deep Learning. Deep learning helps the computer learn more about your use of language by looking at previous questions and the way you responded to the results.
Once the machine totally understands your meaning, then NLG gets to work generating a response that you will understand.
(saying into her watch) “I see a little silhouetto of a man”
(Siri responds by singing ‘Bohemian Rhapsody’)
The future, guys.