NLP: how to analyze, understand, and generate human language with computers
NLP is no longer an unfamiliar concept for many businesses that rely on computers, as it is helping humans communicate with machines and vice versa. NLP is a technology that combines linguistics, artificial intelligence, and computer science to process and analyze large amounts of natural human language in various settings.
People tried to get computers to understand language by building in rules about the way we thought language worked, however we could never get a computer to respond sensibly or take action based on the sentence that was given to it. The reason for that is that language is very complex.
However, in the past few years, there has been an alternative that has shown promise: machine learning. Instead of hard–coding rules, a system is set up to you give the computer many examples of what it should do. The computer then learns how to do the task.
Neural networks, which are a type of machine learning, and transformers (type of neural network) work well since they have a mechanism for looking at sequences. This is great for text. They are also easy to scale, and it is easy to make large versions, which results in good performance.
Data scientists now train systems by feeding them with as much text as possible, rather than writing rules. They provide systems with texts and attempt to get them to predict the next texst. As a result, we have a system that can take in the first half of a sentence and write the second half. It can also take in text and write summaries. Even translation can be done this way, because these are all language issues that can be handled by a system that understands the text, thanks to NLP.
At LenseUp, we focus on:
- Text generation: the system takes in text and then produces more text. This approach can be used for many activities, such as getting a summary, translation, blog, or extracting entities.
- Embeddings: An embedding can be thought of as a vector, or a list of numbers. So, when you give it some text, it outputs a list of numbers that can be used for things such as semantic search or clustering. This is done by measuring the distance within the vector space. This approach is very useful and has many applications such as semantic search for Question / Answer chatbots.
- Multilingual NLP: one of the main reasons why multilingual NLP has not been able to scale quickly is due to the lack of labelled data in low–resource languages. BLOOM is the largest multilingual language model to be trained openly and transparently, which may help to solve this issue. It was released in july 2022. Multilingual NLP, such as we can witness it in models such as Openai’s Whisper, is changing the game!
Ask for a quote
