How do contextual embeddings like BERT differ from traditional embeddings?

Contextual embeddings like BERT differ from traditional embeddings by generating word representations that adapt to their surrounding context, whereas traditional methods assign fixed vectors to words regardless of usage. Traditional embeddings, such as Word2Vec or GloVe, map each word to a static vector based on its overall frequency or co-occurrence patterns in a training corpus. For example, the word “bank” would have the same vector in “river bank” and “bank account,” even though the meanings differ. In contrast, BERT produces dynamic embeddings that reflect how a word functions in a specific sentence. This allows “bank” to have different representations depending on whether it refers to a financial institution or a physical landmark.

The technical difference lies in architecture and training. Traditional embeddings are trained using shallow neural networks or matrix factorization to capture global word relationships. For instance, Word2Vec uses skip-gram or CBOW models to predict neighboring words, while GloVe leverages word co-occurrence statistics. These methods treat words in isolation, ignoring sentence structure. BERT, however, uses transformer layers with self-attention mechanisms to process entire sequences bidirectionally. During training, BERT learns by predicting masked words in sentences and determining if two sentences follow each other. This forces the model to consider context from both directions—left and right—when generating embeddings. For example, in the sentence “She deposited money into her bank account,” BERT’s embedding for “bank” incorporates the words “deposited” and “account,” linking it to finance.

A practical example of this distinction is in disambiguating homonyms. Suppose a developer builds a sentiment analysis model: Traditional embeddings might struggle with the phrase “The bass was too loud,” as “bass” could refer to a fish or a low-pitched sound. BERT, however, would adjust the embedding based on adjacent words like “loud” to infer the correct meaning. Similarly, in entity recognition, BERT can better distinguish “Apple” as a company versus a fruit by analyzing surrounding terms like “stock” or “juice.” While traditional embeddings are computationally lighter and suitable for simple tasks like keyword matching, BERT’s context-aware approach excels in complex NLP tasks like question answering or semantic search, where meaning depends heavily on phrasing. Developers often fine-tune BERT for specific use cases, leveraging its dynamic embeddings to capture nuances that static methods miss.