Natural Language Processing · TEE · papers
Q1C · Word2Vec and GloVe
Solved re-exam · February 2026
Actual re-examination · 9 February 2026 · 100 marks · 3 hours
Answer
Both Word2Vec and GloVe learn dense word vectors from distributional information: words occurring in related contexts tend to receive related representations. They differ mainly in the training objective and how corpus co-occurrence evidence is used.
| Basis | Word2Vec | GloVe |
|---|---|---|
| Learning strategy | Predictive: CBOW predicts a centre word; Skip-gram predicts nearby words | Count-based objective fitted to global word–context co-occurrence statistics |
| Training evidence | Local context windows converted into prediction examples | A corpus-wide co-occurrence matrix and relationships among counts |
| Parameters learned | Word/context embeddings that make context predictions accurate | Word/context embeddings and biases that fit weighted log co-occurrence counts |
| Corpus considerations | Large token sequences with useful local context, including domain-specific text | A sufficiently large corpus from which reliable global co-occurrence statistics can be collected |
For a domain such as medicine, either method can benefit from a representative medical corpus. Word2Vec is convenient when generating local training windows directly from a stream of tokens. GloVe is natural when a global sparse co-occurrence matrix can be constructed and stored. Corpus size, domain match, preprocessing and available compute matter; there is no rule that Word2Vec requires informal text while GloVe requires formal text.
Both standard forms produce one static vector per vocabulary word, so multiple senses are blended. They do not automatically provide a different vector for the same word in each sentence.
Yaad rakho: Word2Vec predicts local neighbours; GloVe fits global co-occurrence counts.