Word Embeddings

What is word embeddings?

Word embeddings are a type of word representation used in natural language processing (NLP) and machine learning. They involve mapping words or phrases to vectors of real numbers in a continuous vector space. The idea is that words with similar meanings will have similar embeddings, making it easier for algorithms to understand and process language.

Here’s a bit more detail on how it works:

1. Vector Representation: Each word is represented as a vector (a list of numbers). For example, the word "king" might be represented by a vector like [0.3, 0.1, 0.7, ...].
2. Semantic Similarity: Words that have similar meanings are mapped to nearby points in the vector space. So, "king" and "queen" would be close to each other, while "king" and "apple" would be further apart.
3. Dimensionality: The vectors are usually of high dimensionality (e.g., 100 to 300 dimensions). Higher dimensions can capture more subtle semantic relationships, but also require more data and computational resources.
4. Training: These embeddings are typically learned from large text corpora using models like Word2Vec, GloVe (Global Vectors for Word Representation), or more advanced techniques like BERT (Bidirectional Encoder Representations from Transformers).

Pre trained word embeddings

Pre-trained word embeddings are vectors that represent words in a continuous vector space, where semantically similar words are mapped to nearby points. They’re generated by training on large text corpora, capturing syntactic and semantic relationships between words. These embeddings are useful in natural language processing (NLP) because they provide a dense and informative representation of words, which can improve the performance of various NLP tasks.

What examples of pre-trained word embeddings?

1. Word2Vec: Developed by Google, it represents words in a vector space by training on large text corpora using either the Continuous Bag of Words (CBOW) or Skip-Gram model.
2. GloVe (Global Vectors for Word Representation): Developed by Stanford, it factors word co-occurrence matrices into lower-dimensional vectors, capturing global statistical information.
3. FastText: Developed by Facebook, it builds on Word2Vec by representing words as bags of character n-grams, which helps handle out-of-vocabulary words better.

Visualizing pre-trained word embeddings can help you understand the relationships and structure of words in the embedding space.

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