selpref-emb

Selectional Preference Embeddings (EMNLP 2017)

This repository contains joint embeddings of selectional preferences, words, and fine-grained entity types.

Download

• lemmatized, basicDependencies

• lemmatized, enhancedDependencies

• lemmatized, enhancedPlusPlusDependencies

• unlemmatized, basicDependencies

• unlemmatized, enhancedDependencies

• unlemmatized, enhancedPlusPlusDependencies

Usage

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

Reference

@InProceedings{D17-1138,
  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/D17-1138"
}