# Q1c · Word2Vec versus Doc2Vec

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/legacy-r23/q1c

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q1c/

Previous examination · complete worked answers

**Question. Differentiate Word2Vec and Doc2Vec.** [5 marks]

### Answer

| Aspect | Word2Vec | Doc2Vec |
| --- | --- | --- |
| Representation | One learned vector per word type | Learns document/paragraph vectors as well as, in variants, word vectors |
| Training | CBOW predicts centre from context; Skip-gram predicts context from centre | PV-DM combines document/context to predict words; PV-DBOW predicts sampled words from document ID |
| Unit | Word/token type | Sentence, paragraph or document |
| New text | Retrieve known word vectors; combine via a separate pooling choice | Infer a new document vector while learned model weights stay fixed |
| Use | Word similarity, features | Document similarity, retrieval or classification |

Averaging Word2Vec word vectors is a possible document representation, but it is not the Doc2Vec training algorithm. This older paper names Doc2Vec explicitly; the current course names word and sentence embeddings but does not explicitly list Doc2Vec.