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dc.creatorHeinzerling, Benjamin
dc.identifierhttps://doi.org/10.11588/data/FJQ4XL
dc.description<p>Joint embeddings of selectional preferences, words, and fine-grained entity types.</p> <p>The vocabulary consists of: <ul> <li> verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" <li> words and short noun phrases, e.g. "Titanic" <li> fine-grained entity types using the FIGER inventory, e.g.: /product/ship or /person/politician </ul> </p> <p> The files are in word2vec binary format, which can be loaded in Python with gensim like this: <code> <pre>from gensim.models import KeyedVectors emb_file = "/path/to/embedding_file" emb = KeyedVectors.load_word2vec_format(emb_file, binary=True)</pre></code> </p>
dc.languageEnglish
dc.publisherheiDATA
dc.subjectComputer and Information Science
dc.titleSelectional Preference Embeddings (EMNLP 2017)


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