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Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell
2018
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
This paper presents an approach for investigating the nature of semantic information captured by word embeddings. We propose a method that extends an existing humanelicited semantic property dataset with gold negative examples using crowd judgments. Our experimental approach tests the ability of supervised classifiers to identify semantic features in word embedding vectors and compares this to a feature-identification method based on full vector cosine similarity. The idea behind this method is
doi:10.18653/v1/w18-5430
dblp:conf/emnlp/SommerauerF18
fatcat:4hoy34el4zgldpfa2vmafz2xpe