title: Word embedding training
body: <p>I have one corpus for word embedding. Using this corpus, I trained my word embedding. However, whenever I train my word embedding, the results are quite different(this results are based on K-Nearest Neighbor(KNN)). For example, in the first training, '<strong>computer</strong>' nearest neighbor words are '<strong>laptops</strong>', '<strong>computerized</strong>' ,'<strong>hardware</strong>'. But, in the second training, this knn words are '<strong>software</strong>', '<strong>machine</strong>',...('laptops' is low ranked!) - all training are performed independently 20 epochs, and hyper-parameters are all the same.</p>
  
  <p>I want to train my word embedding very similar(e.g., 'laptops' is high ranked). How should i do? Should I modulate hyper-parameters(learning rate, initializing, etc)?</p>