Selectional Preference Embeddings (EMNLP 2017)
This repository contains joint embeddings of selectional preferences, words, and fine-grained entity types.
The files are in gensim model format, which can be loaded in Python like this:
from gensim.models import KeyedVectors
emb_file = "/path/to/embedding_file"
emb = KeyedVectors.load_word2vec_format(emb_file, binary=True)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
@InProceedings{D17-1139,
author = "Heinzerling, Benjamin
and Moosavi, Nafise Sadat
and Strube, Michael",
title = "Revisiting Selectional Preferences for Coreference Resolution",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
year = "2017",
publisher = "Association for Computational Linguistics",
pages = "1343--1350",
location = "Copenhagen, Denmark",
url = "http://aclweb.org/anthology/D17-1139"
}