NLP can be expanded to predict words, phrases, or sentences if needed!įor this project, we will specifically be using Markov chains to complete our text. NLP allows us to dramatically cut runtime and increase versatility because the generator can complete words it hasn’t even encountered before. The generator could only complete words that it had seen before.It would be very slow to search thousands of words.
There are two problems with this approach. Without NLP, we’d have to create a table of all words in the English language and match the passed string to an existing word. The text generator project relies on text generation, a subdivision of natural language processing that predicts and generates next characters based on previously observed patterns in language. In other words, we are going to generate the next character for that given string. Our text generator would determine that y is sometimes after e and would form a completed word. We need to find the character that is best suited after the character e in the word monke based on our training corpus.
The text generator will then apply these patterns to the input, an incomplete word, and output the character with the highest probability to complete that word. This will be a character based model that takes the previous character of the chain and generates the next letter in the sequence.īy training our program with sample words, our text generator will learn common patterns in character order. Today, we are going to build a text generator using Markov chains.
These skills are valuable for any aspiring data scientist.
iMessage text completion, Google search, and Google’s Smart Compose on Gmail are just a few examples. You’ve probably encountered text generation technology in your day-to-day life. Even journalism uses text generation to aid writing processes. Text generation is popular across the board and in every industry, especially for mobile, app, and data science. Introduction to the Text Generator Project
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