On word embeddings - Part 3: The secret ingredients of word2vec

An in-depth analysis reveals that word embedding models like word2vec aren't inherently superior to traditional distributional semantic methods, with hyperparameter optimization being more crucial than algorithm choice. The study demonstrates that Singular Value Decomposition (SVD) often outperforms popular embedding methods in word similarity tasks, while Skip-gram Negative Sampling (SGNS) excels in analogy tasks.