A Simple Guide to NLP Vectorization and Vector Databases

Natural language processing solutions are gaining popularity as a way of simplifying and streamlining many tasks involving big data. 

This isn’t to say that the technology and logic behind NLP is straightforward; far from it. 

To help put NLP Vectorization into layman's terms, we have put together this simple guide.

What is NLP?

Natural language processing, or NLP, is the technology that enables machines to understand, analyze and respond to human language.

It is what enables your phone to understand voice commands, chatbots to reply to your queries and for Google to guess what you are searching for.

NLP as a technology helps computers to read, translate and summarize text, making them better at communicating with us in a tone that sounds natural.

What about NLP Vectorization?

To understand what NLP vectorization is, let’s start with an example. Take the sentence “the cat sat on the mat”.

Now, computers do not understand words or context like we do. Instead, they need a way to turn the words into numbers which can be processed by the computer. 

The process of converting words and sentences into numbers is called NLP vectorization, with the sequence of numbers referred to as a vector.

How does vectorization help NLP?

The process of converting words into numbers enables computers to:

• Understand similar words and synonyms
• Find relationships between words
• Quickly process large volumes of textual data

What are vector databases?

Once the words have been converted into vectors, these are stored within specific databases that store the word-number combinations. 

These vector databases are used to help NLP solutions to find the most similar response in the database to an external query. 

For example, if a chatbot user was to ask “how do I get a refund?” and no exact match exists, NLP vectorization is used to find the most similar response using the number lists, such as a response to a similar question such as “how can I return this item?”.

Why does this matter?

NLP vectorization powers many of the digital tools that we incorporate into our everyday lives. 

These technologies are making it possible for machines to interpret meaning, find similarities and to provide accurate, reasoned responses.

Search engines

Traditionally, search engines would rely upon exact match keywords to find what you were looking for. This was useful enough to be useful, but often would ignore close synonyms or similar phrases. 

For example, if you were to search “cheap laptops”, these early search engines would not necessarily display results that focused upon affordable laptops. 

However, with the help of NLP vectorization, search engines can understand the contextual meaning behind your query, meaning that even if you make a typo or phrase something slightly differently, the search engine will still be able to provide relevant results. 

An example of this is the search for “best hotels for digital nomads” will understand that this is contextually similar to “top-rated accommodation for remote workers”, helping to improve the user experience when displaying search results.

Virtual assistant chatbots

Many websites utilize virtual assistant chatbots as ways to ensure 24/7 customer service and support. 

When users chat with the virtual assistant, it is vital that the tool understands the problem, even if it is phrased differently from the norm. Traditional bots would use keyword detection, whereas vector-based NLP chatbots are able to understand context and synonyms. 

Commonly used virtual assistants such as Siri, Alexa and Google Assistant all use NLP vectorization to better understand the commands of the user.

Recommendations

Even platforms such as Netflix and Spotify deploy NLP vectorization to better serve you as the user. 

The recommendations made by these platforms, whether it be the next song to listen to or the next series to binge watch, are personalized to your previous search history. As well as matching the keywords, the context and meaning behind what you search is considered when suggesting you new content that you might enjoy. 

For instance, if you search for “calm piano music”, a recommendation engine powered by NLP will understand the similarities to “relaxing music”, helping the tool to suggest new music relevant to your search intent.

Fraud detection

As well as understanding content, NLP vectorization enables machines to analyze patterns and to identify anything that seems suspicious or out of line. 

Fraud detection is particularly combatted within financial enterprises and can be present within emails, messages and transactions is helping to keep consumers safe by identifying patterns within the activities of the scammers. Even if different phrases are used, NLP systems can be trained to spot the common signs of fraud, as well as phishing, misinformation and fake news.

Final thoughts

To put simply, NLP vectorization has helped us to change how we interact with technology and machines. Whether it is helping search engines to understand your queries, improving the relevancy of chatbot responses, suggesting to you your next favourite television series or enhancing security, 

NLP technology is helping to make computers more intelligent and useful within everyday life. As advances within AI continue to be made, expect to see NLP-powered systems become even more accurate, conversational and intuitive.

If your organisation is looking to benefit from NLP technology, NetGeist’s NLP solutions will not let you down. We create NLP tools that help businesses to tackle textual challenges by automating, processing and summarizing information. Let us simplify your textual tasks with a unique solution, tailored specifically just for you.