Hey there! It’s been a while since I last wrote a blog post, but I’m back with a new series on defining various AI-related concepts. Today, we’re going to talk about word embeddings.

Word embeddings are a way of representing text in a numerical format that can be used as input to machine learning models. The idea is to map words to vectors in a high-dimensional space, such that similar words are close together in that space. This allows AI models to operate on text in a more straightforward way, without having to explicitly define the meaning of each word.

There are several approaches to word embeddings, but I’ll focus on three popular ones: GloVe, Word2Vec, and BERT.

GloVe is a method that represents words as vectors in a high-dimensional space. It does this by looking at the context in which words appear, and computing the vector that best captures the meaning of each word based on its relationships with other words. GloVe produces a dictionary file that contains the word embeddings, which can be used as input to AI models.

Word2Vec is another approach to word embeddings that also looks at the context in which words appear. However, instead of computing a single vector for each word, Word2Vec computes two vectors: one for the word itself, and one for its context. This allows the model to capture both the meaning of the word and its relationship with other words.

BERT (Bidirectional Encoder Representations from Transformers) is a more recent approach to word embeddings that uses a combination of GloVe and Word2Vec. BERT uses a multi-layer bidirectional transformer encoder to generate contextualized representations of words in a sentence. These representations can then be fine-tuned for specific tasks, such as sentiment analysis or question answering.

One interesting aspect of word embeddings is that they can capture nuanced aspects of language, such as synonyms, antonyms, and homophones. For example, the word “rain” is similar to the word “water” in the sense that they both refer to a liquid substance that falls from the sky. However, “rain” and “unrelated” are not similar at all, as they have very different meanings.

I hope this gives you a good introduction to word embeddings! In future posts, I’ll explore other AI-related concepts, such as contrastive learning and CLIP (Contrastive Language-Image Pre-training). If you have any questions or topics you’d like me to cover, please leave a comment below.

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