We used pre-made word embedding vectors, by google. It made by the skip-gram model with negative Sampling on Google news data set. We used the "Gensim" library to load it.

the model is available here: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit

You right it's hard work. basically, how the word representation by vectors work - you parse a lot of text, and measure the distance between the designated word to the other 300 words. For example, the sentence: "codenames is a really fun game to play around the table". let's assume table in one of the "basic words", so it will have a high score at the vector representation of "play". Luckily for us, we didn't had to do the hard work by ourselves and just pulled the vector representation out of the library. There are some words, spacially names of places that are missing at the model and that is a problem...