How are embeddings created for words and sentences?

Embeddings for words and sentences are numerical representations that capture semantic meaning, allowing machines to process language mathematically. For words, embeddings are typically created using neural networks trained on large text corpora. A common approach involves the Word2Vec algorithm, which learns embeddings by predicting surrounding words in a sentence (Skip-Gram) or predicting a target word from its context (Continuous Bag of Words). For example, after training, words like “king” and “queen” end up with similar vector representations because they appear in comparable contexts. Another method, GloVe, constructs embeddings by analyzing global word co-occurrence statistics across a corpus. This captures relationships like “Paris - France + Italy ≈ Rome,” where vector arithmetic reflects semantic analogies.

Sentence embeddings build on word-level techniques but require combining individual word vectors into a single representation. Simple methods average or sum the embeddings of words in a sentence, but this loses word order and context. More advanced approaches use models like BERT or Sentence-BERT, which process entire sentences through transformer architectures. For instance, BERT generates contextual embeddings by analyzing bidirectional relationships between words, allowing the same word (e.g., “bank” in “river bank” vs. “bank account”) to have distinct representations based on context. Sentence-BERT fine-tunes BERT to produce fixed-size sentence embeddings by passing inputs through siamese networks and optimizing for semantic similarity tasks. This enables tasks like finding semantically related sentences in a database.

The choice of method depends on the use case. Word-level embeddings work well for tasks like keyword matching or part-of-speech tagging, while sentence embeddings are better for semantic search or clustering. Tools like Gensim (for Word2Vec) and Hugging Face Transformers (for BERT) provide pre-trained models and APIs to generate embeddings efficiently. For example, using gensim.models.Word2Vec, developers can train custom embeddings on domain-specific data, while sentence-transformers simplifies generating sentence embeddings with just a few lines of code. These techniques form the backbone of modern NLP applications, from chatbots to recommendation systems, by translating language into a form machines can compute with.