Cover Image

Selectional preference embeddings (EMNLP 2017)

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...

Full description

Saved in:
Bibliographic Details
Main Author: Heinzerling, Benjamin (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2019-01-31
DOI:10.11588/data/FJQ4XL
Subjects:
Forschungsdaten
Datenbank
Online Access:Verlag, kostenfrei, Volltext: http://dx.doi.org/10.11588/data/FJQ4XL
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/FJQ4XL
Get full text
Author Notes:Benjamin Heinzerling
Description
Summary: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)
Item Description:Gesehen am 21.02.2019
Physical Description:Online Resource
DOI:10.11588/data/FJQ4XL

Similar Items