Efficient Vector Representation for Documents through Corruption
Table 5: Classification error (%) of a linear classifier trained on various document representations on the Wikipedia dataset.
['Model', 'BOW', 'DEA', 'Word2Vec + AVG', 'Word2Vec + IDF', 'ParagraphVectors', 'Doc2VecC']
We can see that most of the algorithms are not sensitive to the size of the vector representation. Doc2Vec benefits most from increasing representation size. Across all sizes of representations, Doc2VecC outperform the existing algorithms by a significant margin. In fact, Doc2VecC can achieve same or better performance with a much smaller representation vector.