![]() Let's load the text to a string since it's only 701KB, which will fit in memory nowadays. ![]() We have a text file for Pride and Prejudice from Project Gutenberg stored as pg1342.txt in the same folder as our notebook, but also available online directly. Lets see how far we can get with N-grams without outside resources. After we have this dictionary, as a naive example we could actually predict sentences by just randomly choosing words within this dictionary and doing a weighted random sample of the connected words that are part of n-grams within the keys. What we want to do is build up a dictionary of N-grams, which are pairs, triplets or more ( the N) of words that pop up in the training data, with the value being the number of times they showed up. An N-gram is a contiguous ( order matters) sequence of items, which in this case is the words in text. We're actually starting to describe something that uses N-grams. The Easy Way ¶Ĭonversely, the easy way to learn the relationships is by throwing lots of data en masse at a machine, and letting it build up a model of the relationships ( this sounds suspiciously like Machine Learning).Īn even simpler form of that is to track the number of words that are in sequence with one another, and keeping track of the frequency at which this occurs. The right way involves delving deep into semantic networks and ontologies, something I'd touched upon in my climate modelling days, but never mind that We're doing The Easy Way. Turns out there's the right way, and then there's the easy way. How can we get a machine to understand these relationships? One of them is this idea of understanding the relationships between words in sentences. Now, they are obviously much more complex than this tutorial will delve into, but we can touch on some of the core principles. ![]() ![]() I've always wondered how chat bots like Alice work. ![]()
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