Word2vec is a powerful tool for learning word embeddings, with its success attributed to specific architecture decisions. By transferring these choices to traditional distributional methods, they can compete with popular word embedding techniques. This post dives into the reasons behind word2vec’s success and its relationship with traditional models, such as GloVe. The author explores different models, hyperparameters, and results to showcase how neural word embedding models’ success factors can be adapted to traditional methods. The post challenges the notion that word embeddings are superior to distributional methods, emphasizing the importance of tuning hyperparameters and using appropriate pre-processing and post-processing techniques for optimal performance.
https://www.ruder.io/secret-word2vec/