Answer: Word2Vec focuses on word-level embeddings, Sentence2Vec on sentence-level embeddings, and Doc2Vec on document-level embeddings, catering to different granularities of text representation.
Below is a summarized comparison of Word2Vec, Sentence2Vec, and Doc2Vec across various key aspects.
| Feature | Word2Vec | Sentence2Vec | Doc2Vec |
|---|---|---|---|
| Granularity | Word-level | Sentence-level | Document-level |
| Training | Requires large text corpus | Sentence embeddings, typically with deep learning models | Similar to Word2Vec, with document context |
| Usage | Word similarity, word analogies | Sentence similarity, classification | Document similarity, classification |
| Input | Single words | Sentences | Documents |
| Training Speed | Fast | Moderate | Moderate to Slow |
| Dimensionality | Dependent on training data | Depends on sentence length | Dependent on training data |
| Memory Usage | Lower | Moderate | Higher |
This table provides a concise comparison of various aspects of Word2Vec, Sentence2Vec, and Doc2Vec.
Conclusion:
In conclusion, after conducting a comprehensive analysis, it’s evident that each method—Word2Vec, Sentence2Vec, and Doc2Vec—offers unique advantages based on the granularity of text representation required. The choice among them depends on the specific task and the level of context needed, ranging from word-level semantics to document-level understanding.