@SCMatt and @ccollier are correct, it stands for natural language processing, but it’s not like machines actually understand language - surprisingly, creating AI that can beat people in GO, walk like dogs, somewhat drive cars, etc. were easier to solve than solving language. The reason for this is that language is always changing, there are always exceptions to every grammatical rule, words can change meaning based on context and a single word can change the meaning of entire sentences. Language is a creative process where we can invent new words and sentences and others will still be able to understand us.
The term “natural” is actually misleading as well: in theory, it is possible to create language tools that work well on all languages, in reality, you have to really tweak them or develop new ones based on the complexity and grammar of the language (hence the Pali NLP ideas).
Now this might sound like rocket science, but in reality, all these tools can do (since we have no better idea on how to tackle language with computers) is treat letters/words/sentences as numerical values and work with those values in mathematical models. If you write an essay in Word, there’s an indicator on the bottom that shows how many key presses or letters the essay has. As a human, you would have a really hard time counting all letters, but this is super easy for a computer to do. For this reason, NLP tools rely on solutions that are easier for computers to do, even if humans would never analyze a text based on letter frequency for instance. However, these seemingly strange methods can still yield a lot of information: for example it’s possible to tell by examining word frequencies and distribution, if two texts were written by the same person. That’s how J.K. Rowling was revealed to be a writer of a book other than Harry Potter.
Running NLP tasks on Suttas could boost the efficiency of online search. However, it doesn’t feel quite right to treat the Suttas as mere numbers.