Word2Vec — Word Embeddings
Word2Vec Word Embeddings · Also known as: word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.
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When to use it
Use Word2Vec when you have a large unlabelled text corpus and want dense vector representations that capture semantic similarity between words. It suits exploratory and relational analysis of text. A reasonably large corpus (roughly a hundred documents or more, ideally far larger) is required; on small corpora the model cannot learn meaningful embeddings and pretrained vectors such as GloVe are the better choice. With no text data at all, word embeddings cannot be built.
Strengths & limitations
- Captures semantic similarity so related words sit close together in vector space.
- Produces compact dense vectors that serve as numeric features for downstream text tasks.
- Learns from unlabelled text alone — no manual annotation is needed.
- Needs a large text corpus; small corpora yield unstable, meaningless embeddings.
- Requires careful text preprocessing before training.
- Assigns one vector per word, so it cannot distinguish different senses of the same word from context.
Frequently asked
What is the difference between skip-gram and CBOW?
Both are shallow neural architectures for learning embeddings. Skip-gram predicts the surrounding context words from a given target word, while continuous bag-of-words (CBOW) predicts the target word from its surrounding context. They produce comparable embeddings, with skip-gram often working better on smaller corpora and rare words.
How large does my corpus need to be?
Word2Vec learns purely from co-occurrence, so it needs a sizeable corpus. On very small text (under about a hundred documents) it cannot learn meaningful vectors; in that case pretrained embeddings such as GloVe are the safer choice.
How do I measure similarity between two words?
Compare their learned vectors with cosine similarity. Words used in similar contexts get similar vectors, so a high cosine similarity indicates semantically related words.
Can Word2Vec tell apart different meanings of the same word?
No. It assigns a single static vector per word, blending all senses together. Distinguishing word senses from context requires contextual embedding models rather than Word2Vec.
Sources
- Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. link ↗
How to cite this page
ScholarGate. (2026, June 1). Word2Vec Word Embeddings. ScholarGate. https://scholargate.app/en/text-mining/word2vec
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Document ClusteringText mining↔ compare
- GloVe EmbeddingsText mining↔ compare
- Text ClassificationText mining↔ compare
- TF-IDFText mining↔ compare