What is Natural Language Processing?
Fifty years ago, the idea that computers could possibly interpret and understand human language was little more than a fantasy. Today, natural language processing (NLP) allows machines to analyze and comprehend written and spoken language, providing data scientists with endless opportunities! But what is natural language processing, exactly? And how does it work? Learn more in this glossary entry and find out how The Data Incubator’s programs and bootcamps develop your skills and help you become a successful data scientist.
What Does NLP Mean?
NLP is a branch of computer science that allows computers to process and understand human language. It allows computers to perform automated language-related tasks that don’t require human intervention, such as:
- Checking text for spelling errors
- Responding to human conversations
- Measuring sentiment and intent in social media messages and other forms of text
- Hearing and interpreting speech
- Deciding which parts of a piece of text are the most important
A good example of NLP in action is ChatGPT, which you’re almost certainly used by now. This NLP tool uses artificial intelligence to understand the prompts you type and generate human-like responses to any questions you have.
NLP dates back to the 1940s, when scientists hoped to create machines that could automatically translate text. However, it’s only been possible in the last decade for computers to extract information from text and accurately interpret it.
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How Does NLP Work?
Just like a child learns the fundamentals of language, NLP can understanding the meaning of language over time by collecting vast amounts of text and speech data. NLP models then find relationships in text and speech and can understand the different components of human language. These components include word meanings (semantics), sentence structure (syntax), and so on.
There are various NLP techniques that you need to know about:
- Sentiment analysis happens when NLP models identify positive, negative, and neutral sentiments, usually in text.
- Keyword extraction occurs when NLP models identify the most relevant or valuable keywords in a piece of text.
- Summarization involves NLP models summarizing long pieces of text for readers.
- Tokenization happens when NLP models break down text elements like words into smaller pieces called tokens. That allows computer programs to process text more successfully.
Pros of NLP
Here are some of the advantages of NLP for budding data scientists like you:
- Sentiment analysis lets you establish whether social media posts are positive, negative, or neutral, which can help you identify what people think about your company.
- Keyword extraction lets you determine the most valuable keywords within a long piece of text, allowing you to understand the nature of that text without reading it from start to finish. That can be useful when preparing data science reports.
- Tokenization can improve data analysis by breaking down a text into smaller chunks so analytical tools can process that text more effectively.
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Cons of NLP
Here are a few disadvantages of NLP:
- NLP models sometimes struggle with words and phrases with the same meaning, making tasks like sentiment analysis difficult.
- Misspelled or incorrect words in a text can also pose problems for NLP models.
- NLP models almost always focus on the world’s most-used languages, meaning fewer resources are available for less-spoken languages.
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