Comparing Word2Vec, Sentence2Vec, and Doc2Vec: A Comprehensive Analysis

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.