Natural Language Processing
Word Embeddings

Word Embeddings

What are Word Embeddings?

Word embeddings are dense vector representations of words that capture their semantic meaning and contextual relationships within a continuous space. These embeddings enable AI systems to perform arithmetic operations on words, discover semantic similarities, and better understand linguistic patterns.

Word embeddings serve as input for various NLP tasks, including sentiment analysis, machine translation, and text classification, and have significantly improved the performance of language models in recent years.

Techniques for Creating Word Embeddings

Several techniques exist for creating word embeddings, such as:

  • Word2Vec: Developed by researchers at Google, Word2Vec is an unsupervised learning algorithm that generates word embeddings using either the continuous bag-of-words (CBOW) or the skip-gram model. Word2Vec embeddings capture semantic and syntactic relationships between words based on their co-occurrence patterns in large text corpora.
  • GloVe (Global Vectors for Word Representation): Developed by researchers at Stanford University, GloVe is another unsupervised learning algorithm that creates word embeddings by leveraging both global and local co-occurrence patterns in text data. GloVe embeddings are known for their ability to capture linear relationships between words and their semantic properties.
  • FastText: Developed by Facebook AI Research, FastText is an extension of the Word2Vec algorithm that generates word embeddings by considering not only the word itself but also its subword components (n-grams). FastText is particularly useful for languages with complex morphology and can handle out-of-vocabulary words more effectively than Word2Vec or GloVe.

Example of Word Embeddings

Suppose we have the following word embeddings for three words: "king," "queen," and "man."

  • king: [1.2, 0.9, 0.7]
  • queen: [1.1, 0.8, 0.6]
  • man: [0.9, 0.7, 0.5]

Using these embeddings, we can perform arithmetic operations to uncover semantic relationships. For instance, we can compute the vector for "woman" by subtracting the vector for "man" from the vector for "king" and adding the vector for "queen":

  • woman = king - man + queen
  • woman = [1.2, 0.9, 0.7] - [0.9, 0.7, 0.5] + [1.1, 0.8, 0.6]
  • woman = [1.4, 1.0, 0.8]

In this example, the resulting vector for "woman" is close to the vector for "queen," which demonstrates the ability of word embeddings to capture semantic relationships and analogies (king:queen :: man:woman).