Word Embendding

Word embeddings are a method of representing words as lists of numbers, or vectors. These vectors capture relationships between words, allowing machine learning models to understand concepts like similarity and context. For example, "kigali" and "nairobi" would have similar vectors because both are cities. Similarly, "king" and "queen" would have related vectors since they are both related to royalty.

Word2Vec is one of the most popular embedding methods. It learns vector representations of words by analyzing large amounts of text data. Two main architectures within Word2Vec are Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts a target word based on its surrounding context, while Skip-gram does the opposite - it uses a target word to predict its surrounding words. By training on extensive text corpora, Word2Vec learns useful relationships between words.

To experiment with Word2Vec in Python, you can use the gensim library. You provide it with a list of sentences and specify parameters like the number of dimensions for each word vector (vector_size), the number of neighboring words considered as context (window), and the minimum word frequency (min_count). The most_similar() function can then be used to find words with vectors close to a given word, like "cat" in this example.

However, it's essential to remember that word embeddings are only as good as the data they were trained on. If the training corpus contains biases or inaccuracies, the resulting embeddings will likely reflect those shortcomings. Additionally, the individual numbers within an embedding don't have straightforward human-readable interpretations. Instead, the overall vector represents a complex network of relationships learned from the training data.