import numpy as npįrom sklearn.feature_extraction.text import CountVectorizerįrom sklearn.feature_extraction.text import TfidfVectorizerĪfter importing, we define a function as follows.
Then we’ll see an example of how we can use it to find the similarity between two vectors. It is a fundamental concept in NLP.įinding cosine similarity between two vectorsįirst, we implement the above-mentioned Cosine similarity formula using Python code. These tasks include Semantic Textual Similarity (STS), Question-Answering, document summarization, etc. When we divide the dot product by the magnitude, we get the Cosine of the angle between them.Ĭosine similarity is very useful in NLP for a lot of tasks. The numerator denotes the dot product or the scalar product of these vectors and the denominator denotes the magnitude of these vectors. In the above formula, A and B are two vectors. The formula for calculating Cosine similarity is given by And if we get a cosine similarity value of -1 it means they are not similar. Whereas the value of -1 indicates that the vectors into consideration are perfectly dissimilar or opposite to each other.Īll of the above math that we have gone through means that when we check two documents for similarity or how similar they are and if we get a cosine similarity value of +1 it means that the documents are similar. The value of +1 indicates that the vectors into consideration are perfectly similar. The value of cosine similarity always lies between the range -1 to +1. We will need this information later on.Ĭosine similarity helps in measuring the cosine of the angles between two vectors. Recollect from high school maths that the cosine or the cos is 1 when the angle is 0, cos of 90 degrees in 0, and cos of 180 degrees is -1. We take the cosine measure of the angle between the vectors.
This means that we can find and measure the angle between two vectors and conclude if two vectors are similar or not. One important thing to note about vectors in geometrical representations is that they have both magnitude and direction. They are also represented in Trigonometry and when we plot two vectors we can measure the angle between them. We know that vectors are one-dimensional arrays. We have learned about vectors and matrices.
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