  We present a new method for estimating vector space representations of words:
embedding learning by concept induction. We test this method on a highly
parallel corpus and learn semantic representations of words in 1259 different
languages in a single common space. An extensive experimental evaluation on
crosslingual word similarity and sentiment analysis indicates that
concept-based multilingual embedding learning performs better than previous
approaches.
