1. A computer-implemented method, comprising: receiving, at a server having one or more processors, labeled training data including a plurality of groups of words, each group of words having a predicate word, each word having generic word embeddings; extracting, at the server, the plurality of groups of words in a syntactic context of their predicate words; concatenating, at the server, the generic word embeddings to create a high dimensional vector space representing features for each word; obtaining, at the server, a model having a learned mapping from the high dimensional vector space to a low dimensional vector space and learned embeddings for each possible semantic frame in the low dimensional vector space; and outputting, by the server, the model for storage, the model being configured to identify a specific semantic frame for an input.