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This paper presents an approach for investigating the nature of semanticinformation captured by word embeddings. We propose a method that extends anexisting human-elicited semantic property dataset with gold negative examplesusing crowd judgments. Our experimental approach tests the ability ofsupervised classifiers to identify semantic features in word embedding vectorsand com- pares this to a feature-identification method based on full vectorcosine similarity. The idea behind this method is that properties identified byclassifiers, but not through full vector comparison are captured by embeddings.Properties that cannot be identified by either method are not. Our resultsprovide an initial indication that semantic properties relevant for the wayentities interact (e.g. dangerous) are captured, while perceptual information(e.g. colors) is not represented. We conclude that, though preliminary, theseresults show that our method is suitable for identifying which properties arecaptured by embeddings.

Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell