Introducing new developer-friendly pricing
Hey there, developers! At Algolia, we believe everyone should have the opportunity to bring a best-in-class search experience ...
VP of Product Growth
Hey there, developers! At Algolia, we believe everyone should have the opportunity to bring a best-in-class search experience ...
VP of Product Growth
Eye-catching mannequins. Bright, colorful signage. Soothing interior design. Exquisite product displays. In short, amazing store merchandising. For shoppers in ...
Search and Discovery writer
Ingesting data should be easy, but all too often, it can be anything but. Data can come in many different ...
Staff Product Manager, Data Connectivity
Everyday there are new messages in the market about what technology to buy, how to position your company against the ...
Chief Strategic Business Development Officer
Done any shopping on an ecommerce website lately? If so, you know a smooth online shopper experience is not optional ...
Sr. SEO Web Digital Marketing Manager
It’s hard to imagine having to think about Black Friday less than 4 months out from the previous one ...
Chief Strategic Business Development Officer
What happens if an online shopper arrives on your ecommerce site and: Your navigation provides no obvious or helpful direction ...
Search and Discovery writer
In part 1 of this blog-post series, we looked at app interface design obstacles in the mobile search experience ...
Sr. SEO Web Digital Marketing Manager
In part 1 of this series on mobile UX design, we talked about how designing a successful search user experience ...
Sr. SEO Web Digital Marketing Manager
Welcome to our three-part series on creating winning search UX design for your mobile app! This post identifies developer ...
Sr. SEO Web Digital Marketing Manager
National No Code Day falls on March 11th in the United States to encourage more people to build things online ...
Consulting powerhouse McKinsey is bullish on AI. Their forecasting estimates that AI could add around 16 percent to global GDP ...
Chief Revenue Officer at Algolia
How do you sell a product when your customers can’t assess it in person: pick it up, feel what ...
Search and Discovery writer
It is clear that for online businesses and especially for Marketplaces, content discovery can be especially challenging due to the ...
Chief Product Officer
This 2-part feature dives into the transformational journey made by digital merchandising to drive positive ecommerce experiences. Part 1 ...
Director of Product Marketing, Ecommerce
A social media user is shown snapshots of people he may know based on face-recognition technology and asked if ...
Search and Discovery writer
How’s your company’s organizational knowledge holding up? In other words, if an employee were to leave, would they ...
Search and Discovery writer
Recommendations can make or break an online shopping experience. In a world full of endless choices and infinite scrolling, recommendations ...
Feb 18th 2022 product
Natural language understanding, also known as NLU, is a term that refers to how computers understand language spoken and written by people. Yes, that’s almost tautological, but it’s worth stating, because while the architecture of NLU is complex, and the results can be magical, the underlying goal of NLU is very clear.
For example, there are an estimated 320 billion emails sent every day. That is a lot of natural language created and consumed, and if computers can better understand it, it can help the people who are interacting with those emails. NLU can determine whether an email is spam, if an email is high priority, or if there are other, related, emails to share with the recipient. All of these efforts help people get the most out of email.
Of course, there’s also the ever present question of what the difference is between natural language understanding and natural language processing, or NLP. The answer, again, is in the name. Natural language processing is about processing natural language, or taking text and transforming it into pieces that are easier for computers to use. Some common NLP tasks are removing stop words, segmenting words, or splitting compound words. NLP can also identify parts of speech, or important entities within text.
Getting back to the uses of natural language understanding, we can think of other examples, such as:
These examples are a small percentage of all the uses for natural language understanding. Anything you can think of where you could benefit from understanding what natural language is communicating is likely a domain for NLU.
Natural language understanding is complicated, and seems like magic, because natural language is complicated. Language packs a lot of information in a small amount of space. A clear example of this is the sentence “the trophy would not fit in the brown suitcase because it was too big.” You probably understood immediately what was too big, but this is really difficult for a computer.
We can’t simply write a program that checks for the phrase “was too big” and understand that the phrase refers to the first item. First, because the phrase might instead be “was too large” or “was too heavy” or “is too big.” Second, because there are formulations where that “rule” falls flat, such as “the brown suitcase would not fit the trophy because it was too big.” There are even phrasings that might even be confusing to people, such as “I didn’t bring the trophy in the brown suitcase because it was too big.” Was the trophy too big for the suitcase, or was the suitcase too big to bring?
It’s for this reason that NLU relies heavily on machine learning. Machine learning, or ML, can take large amounts of text and learn patterns over time. This is explained by what’s called the distributional hypothesis, which says that you can learn a lot about a word “by the company it keeps.” Take the word “hat.” An ML model might see phrases like, “the man was wearing a hat on his head” or “I put on a hat to keep the sun out of my eyes.” If the model sees phrases like these enough, it starts to pick up on some patterns. Throw it, then, the phrase, “I put on a baseball cap to keep out the sun” and it can sense that just maybe there is a similarity between “hat” and “baseball cap.” Add in the phrase “the man wore a baseball cap on his head” and the similarity is seen to be even stronger.
As you can imagine, these ML models require a lot of data. OpenAI trained their GPT-2 model on 1.5 billion parameters, and followed that up with GPT-3 on 175 billion parameters. This data is often crawled from publicly available data on the web, but is then fine-tuned on a specific dataset. This fine tuning allows the model to better understand a given dataset. For example, fine tuning may help the model to better understand medical data.
Improvements in computing and machine learning have increased the power and capabilities of NLU over the past decade. We can expect over the next few years for NLU to become even more powerful and more integrated into software.
For more information on the applications of Natural Language Understanding, and to learn how you can leverage Algolia’s search and discovery APIs across your site or app, please contact our team of experts.
Powered by Algolia Recommend