Joint embeddings of selectional preferences, words, and fine-grained entity types.

The vocabulary consists of:

• verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj"
• words and short noun phrases, e.g. "Titanic"
• fine-grained entity types using the FIGER inventory, e.g.: /product/ship or /person/politician

The files are in word2vec binary format, which can be loaded in Python with gensim like this:

from gensim.models import KeyedVectors

emb_file = "/path/to/embedding_file"
emb = KeyedVectors.load_word2vec_format(emb_file, binary=True)