How does NLP work?
Posted: Sat Feb 08, 2025 8:39 am
The objective of NLP is to allow computers to “understand” natural human language—that is, language as it is commonly used by people—and to act on it in an appropriate manner.
This means that we are able to use and interact with computer programs without having to learn code, and computers are able to respond to our input with the same kind of language we use.
You might be familiar with the concept in the form of virtual assistants like Siri or Alexa, which use speech-to-text technology to process a wide variety of requests from playing songs to noting down appointments in your calendar.
Of course, that’s not all that NLP can do. Natural language lebanon mobile database processing has a wide range of applications today, some of which can be startling to someone unfamiliar with the current state of AI. Examples include summarizing long texts, drafting emails, and even writing short stories based on prompts!
LaMDA, in particular, was built for dialogue (it’s in the name after all—Language Model for Dialogue Applications), which is why it was so good at responding in conversation.
Computers face the challenge of how to deal with the complex, unstructured mass of data that is human language. Computers don’t communicate the way we do, but with the latest developments in deep machine learning technology they are becoming more and more able to approximate it.
Scientists do the work of training an NLP engine to be able to “understand” human speech through a step-by-step process:
Tokenization. This refers to the process of reducing a text into smaller parts called “tokens”. Tokens are often single words, though they can be larger or smaller units as well. These are the building blocks for the way computers will process natural language.
This means that we are able to use and interact with computer programs without having to learn code, and computers are able to respond to our input with the same kind of language we use.
You might be familiar with the concept in the form of virtual assistants like Siri or Alexa, which use speech-to-text technology to process a wide variety of requests from playing songs to noting down appointments in your calendar.
Of course, that’s not all that NLP can do. Natural language lebanon mobile database processing has a wide range of applications today, some of which can be startling to someone unfamiliar with the current state of AI. Examples include summarizing long texts, drafting emails, and even writing short stories based on prompts!
LaMDA, in particular, was built for dialogue (it’s in the name after all—Language Model for Dialogue Applications), which is why it was so good at responding in conversation.
Computers face the challenge of how to deal with the complex, unstructured mass of data that is human language. Computers don’t communicate the way we do, but with the latest developments in deep machine learning technology they are becoming more and more able to approximate it.
Scientists do the work of training an NLP engine to be able to “understand” human speech through a step-by-step process:
Tokenization. This refers to the process of reducing a text into smaller parts called “tokens”. Tokens are often single words, though they can be larger or smaller units as well. These are the building blocks for the way computers will process natural language.