Selectional Preference Embeddings (EMNLP 2017)
| dc.creator | Heinzerling, Benjamin | |
| dc.date | 2019-01-31 | |
| dc.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) ` | |
| dc.identifier | https://doi.org/10.11588/data/FJQ4XL | |
| dc.language | English | |
| dc.publisher | heiDATA | |
| dc.subject | Computer and Information Science | |
| dc.title | Selectional Preference Embeddings (EMNLP 2017) | |
| dcterms.accessRights | openAccess |