Selectional preference embeddings (EMNLP 2017) - Heinzerling, Benjamin (Universität, 2019)
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)