Natural Language Processing · TEE · papers
Q1c · Word2Vec versus Doc2Vec
2023-01-30 · re-exam · recovered AI paper
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.