Doc2Vec: Not Always the Best Choice for Document Embeddings
Introduction
Doc2Vec, a popular technique for generating document embeddings, is often touted as a superior approach compared to simple averaging or summing of Word2Vec vectors. However, recent research and empirical evidence suggest that this might not always be the case. In certain scenarios, simpler methods like averaging or summing Word2Vec vectors can outperform Doc2Vec.
Understanding Doc2Vec
Doc2Vec, also known as Paragraph Vectors, is a neural network-based technique that learns distributed representations for documents. It extends Word2Vec by introducing a “paragraph vector” that represents the entire document.
When Doc2Vec Might Fall Short
- Short Documents: For very short documents, the added complexity of Doc2Vec might not be necessary. Averaging or summing Word2Vec vectors can achieve comparable results with less computational overhead.
- Simple Tasks: In tasks that do not require capturing intricate semantic relationships between words within a document, simpler methods might suffice. For instance, document classification based on broad themes.
- Limited Training Data: Doc2Vec requires a significant amount of training data to learn robust document representations. With limited data, simpler methods might generalize better.
Experimental Evidence
Several studies have shown that averaging or summing Word2Vec vectors can outperform Doc2Vec in certain situations. A study by [Author, Year] compared Doc2Vec to mean Word2Vec on a text classification task and found that the mean Word2Vec approach achieved better accuracy. Another study by [Author, Year] investigated the performance of various document embedding techniques and concluded that, in some cases, simple averaging of Word2Vec vectors provided comparable results to Doc2Vec.
Example Code: Average Word2Vec
import gensim.downloader as api from gensim.models import Word2Vec # Load pretrained Word2Vec model model = api.load("word2vec-google-news-300") # Define a document document = "This is a sample document about natural language processing." # Split the document into words words = document.split() # Calculate the mean of Word2Vec vectors vector = [model[word] for word in words if word in model.vocab] average_vector = sum(vector) / len(vector)
print(average_vector)
Conclusion
While Doc2Vec has its merits, it is not a universal solution for document embedding. For specific tasks and datasets, simpler methods like averaging or summing Word2Vec vectors can provide comparable or even superior performance. The choice of embedding technique should be guided by the specific task, data characteristics, and computational constraints.