Word embeddings are numeric vectors that represent words or other text so a machine-learning model can process them. In self-supervised learning, the model learns those representations from ordinary text by predicting words or other information from context, rather than relying on people to label every training example. Word2vec learns one fixed vector per vocabulary word; BERT builds a representation for each token occurrence using its surrounding sentence.
What are word embeddings?
An embedding maps an item—such as a word, token, or sentence—to a list of numbers called a vector. The model can use that vector as a compact representation for later tasks. In a word embedding space, words that appear in similar contexts may end up near one another because the training objective has shaped the geometry that way. Google’s machine-learning guide to embeddings explains the idea of learning these representations.
The dimensions usually do not correspond to neat, human-readable properties. And closeness is not proof that two words mean exactly the same thing: it reflects patterns learned from the training data and objective. A vector’s usefulness depends on what a later system needs to do with it.
How does word2vec learn a vector?
Word2vec learns representations through a context-prediction task. Given a word, a model can learn to predict nearby words; alternatively, it can use nearby words to predict the central word. Repeated across text, those prediction examples adjust the model’s weights. The learned weights used to represent words become the vectors.
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This makes the text itself an implicit source of supervision. For example, a sentence containing “The dog chased the ball” supplies nearby-word relationships without a person tagging each relationship as a training label. The Stanford-hosted Speech and Language Processing textbook describes this connection between word2vec’s prediction objective and learning from context.
That is the practical sense of self-supervised learning in natural-language processing: the model creates its learning signal from structure already present in the input. It is not learning without data or objectives; rather, ordinary text provides examples for a prediction task without requiring manual annotation for each one.
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What is the difference between word2vec and BERT embeddings?
The central difference is whether a word receives one fixed representation or a representation that depends on its occurrence in context.
| Aspect | Word2vec and GloVe | BERT |
|---|---|---|
| What gets represented | One fixed vector for each vocabulary word. | A token occurrence represented in the context of its sentence. |
| Learning signal | Prediction of words in nearby context. | Prediction of selected, masked or altered tokens from left and right context. |
| Ambiguous words | The same word has the same vector across different senses. | The representation can vary with surrounding words. |
Consider “bank” in “bank deposit” and “river bank.” Google Research’s BERT documentation notes that context-free methods such as word2vec and GloVe give the vocabulary item “bank” the same representation in both cases. A contextual model uses sentence context, so it can represent the two occurrences differently.
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How does BERT use self-supervision?
BERT is pretrained with masked language modeling. In the procedure described by Google Research, 15% of the input words are selected for prediction; the model processes the sequence with a bidirectional Transformer encoder and predicts the selected words using context on both sides. See the BERT README for its description.
A 2026 survey describes a particular BERT-style masking recipe: among the selected tokens, 80% are replaced with [MASK], 10% with a random token, and 10% are left unchanged. These proportions describe that recipe, not every self-supervised language model. The broader principle is that a model is trained to recover selected information from the text it was given.
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Can self-supervised learning create sentence embeddings?
Yes. Sentence-level methods can learn representations using text without labeled sentence pairs. Examples include contrastive learning, which trains representations by comparing related and unrelated examples, and denoising autoencoding, which trains a model to reconstruct text after corruption.
Unsupervised does not mean universally effective. Sentence Transformers’ documentation cautions that these approaches can perform rather poorly compared with methods trained on pairs. If the target corpus differs from the data used to train a model, domain adaptation may help, but the right choice depends on the task and evaluation data.
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What are embeddings used for?
Embeddings can provide useful inputs to systems that need to compare or group text. OpenAI’s overview lists semantic search, clustering, topic modeling, and classification among text-embedding applications. In semantic search, a system can compare a query vector with document vectors—often using cosine similarity—so relevant results need not repeat the query’s exact keywords. See OpenAI’s introduction to text and code embeddings.
- Semantic search: Find documents with related content, not just matching terms.
- Clustering: Group items whose representations are similar.
- Topic modeling: Help organize text by patterns in its learned representations.
- Classification: Supply a numeric representation to a model that assigns a category.
Similarity is a signal for a downstream system, not a guarantee of factual accuracy, causality, or interchangeable meaning. Results depend on the model, training corpus, task, and evaluation method. A vector can reflect patterns in its data even when those patterns do not establish whether a claim is true.
Which kind of embedding should you choose?
- Use static word vectors when a compact, fixed representation per vocabulary item suits the task and the distinction between word senses is not essential.
- Use contextual representations when the same word needs different representations depending on its sentence.
- Evaluate sentence embeddings on your data when comparing whole sentences or documents. Unsupervised training is an option, but documentation warns it may underperform approaches trained with pairs.
For a deeper technical treatment of word2vec and language representations, see the Stanford-hosted Speech and Language Processing textbook.
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