Exercise

# 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.

Instructions

**100 XP**

- 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`

.