Selectional Preference Embeddings (EMNLP 2017)
Abstract
Description
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)
`
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