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)
