Imagine you are a traveler armed with just a list of city names. A bit overwhelming, isn't it? Without a map, deciphering their relative positions becomes a challenge. Enter coordinates—latitude and longitude. These numerical markers not only identify a city's exact location but also allow you to measure distances between them with ease.
In the realm of machine learning, understanding the subtleties of human language presents a similar puzzle. Words by themselves offer limited insight. For instance, merely encountering "king" or "queen" will not give a machine learning algorithm a clear understanding of their significance or relationship. This is where the power of word embeddings comes to the fore. Techniques like Word2Vec act as advanced language cartographers. For each word, it crafts high-dimensional vectors (imagine a space with more dimensions than our familiar three-dimensional world). These vectors (word embeddings) encapsulate the semantic meanings and associations between words. In this space, similar words cluster together; "king" and "queen" become close neighbors, while "king" and "banana" find themselves much farther apart.