Measuring word vector similarity

In this lesson we will understand the power of word vectors using real world trained word vectors. These are word vectors extracted from a list of word vectors published by the Stanford NLP group. A word vector is a sequence or a vector of numerical values. For example, dog = (0.31, 0.92, 0.13)

The distance between word vectors can be measured using a pair-wise similarity metric. Here we will be using sklearn.metrics.pairwise.cosine_similarity. Cosine similarity produces a higher values when the element-wise similarity of two vectors is high and vice-versa.

Print the length of the cat_vector using ndarray.size attribute.
Compute and print the similarity between the cat_vector and window_vector using cosine_similarity.
Compute and print the similarity between the cat_vector and dog_vector using cosine_similarity.

Exercise

Measuring word vector similarity

Instructions