## Euclidean Distance

Euclidean distance is a measure of the straight-line distance between two points in an n-dimensional space.

It is calculated using the formula:

\( \text{Distance} = \sqrt{ \sum_{i=1}^{n} (x_i - y_i)^2 } \),

where \( x_i \) and \( y_i \) are the coordinates of two points.

In NLP, Euclidean distance is used to compare word or document embeddings in a vector space, indicating how similar or dissimilar two vectors are. A smaller distance suggests higher similarity, while a larger distance indicates dissimilarity. Euclidean distance is intuitive and easy to compute, making it popular for clustering and nearest-neighbor searches.

However, it is sensitive to the scale of features and does not work well with high-dimensional sparse data, where distance measures can become distorted.