Probably you didn’t know what Pouteria was, but I bet you already realized it is a tree. In particular, we know that the meaning of a word is similar to the one of another word if we can interchange them, that’s called the distributional hypothesis.įor example, imagine that you read that “Pouteria is widespread throughout the tropical regions of the world and monkeys eat their fruits”. The result is that our program will understand them as two completely different pieces of information! We, as humans, would never have that problem.Įven when we do not know a word, we can guess what it means by knowing the context where it’s used. But if you compare the vectors that one hot encoders generate from these sentences, the only thing you would find is that there is no word match between both phrases. Our intuition tells us that they are basically the same. Our algorithm should be able to understand that the information in that sentence is very similar to the information in “apes consume fruits”. However, imagine that we’re trying to understand what an animal eats from analyzing text on the internet, and we find that “monkeys eat bananas”. So simple, and yet it works! Machine learning algorithms are so powerful that they can generate lots of amazing results and applications. And yes, you guessed right: the one for Banana. Imagine our entire vocabulary is 3 words: Monkey, Ape and Banana. ![]() Then, you define the vector of the i-th word as all zeros except for a 1 in the position i. ![]() You count how many words there are in the vocabulary, say 1500, and establish an order for them from 0 to that size (in this case 1500). In machine learning, this is usually defined as all the words that appear in your training data. The most straightforward way to encode a word (or pretty much anything in this world) is called one-hot encoding: you assume you will be encoding a word from a pre-defined and finite set of possible words. What does it mean to represent a word? And more importantly, how do we do it? If you are asking yourself those questions, then I’m glad you’re reading this post. But wait, don’t celebrate so fast, it’s not as easy as assigning a number to each word, it’s much better if that vector of numbers represents the words and the information provided. That’s called text vectorization and you can read more of it in this beginner's guide. Sounds great! But there’s a challenge that jumps out: we, humans, communicate with words and sentences meanwhile, computers only understand numbers.įor this reason, we have to map those words (sometimes even the sentences) to vectors: just a bunch of numbers. In contrast to convergent and divergent boundaries, crust is cracked and broken at transform margins, but is not created or destroyed.In Natural Language Processing we want to make computer programs that understand, generate and, more generally speaking, work with human languages. Earthquakes are common along these faults. Rocks that line the boundary are pulverized as the plates grind along, creating a linear fault valley or undersea canyon. Natural or human-made structures that cross a transform boundary are offset - split into pieces and carried in opposite directions. One of the most famous transform plate boundaries occurs at the San Andreas fault zone, which extends underwater. Two plates sliding past each other forms a transform plate boundary. Thus, at convergent boundaries, continental crust is created and oceanic crust is destroyed. Magma rises into and through the other plate, solidifying into granite, the rock that makes up the continents. The Pacific Ring of Fire is an example of a convergent plate boundary.Īt convergent plate boundaries, oceanic crust is often forced down into the mantle where it begins to melt. A chain of volcanoes often forms parallel to convergent plate boundaries and powerful earthquakes are common along these boundaries. The impact of the colliding plates can cause the edges of one or both plates to buckle up into a mountain ranges or one of the plates may bend down into a deep seafloor trench. When two plates come together, it is known as a convergent boundary. The Mid-Atlantic Ridge is an example of divergent plate boundaries. Along these boundaries, earthquakes are common and magma (molten rock) rises from the Earth’s mantle to the surface, solidifying to create new oceanic crust. The Earth’s lithosphere, which includes the crust and upper mantle, is made up of a series of pieces, or tectonic plates, that move slowly over time.Ī divergent boundary occurs when two tectonic plates move away from each other. This image shows the three main types of plate boundaries: divergent, convergent, and transform. ![]() ![]()
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