In recent years, distributional models (DMs) have shown great success in representing lexical semantics. In this work we show that the extent to which DMs represent semantic knowledge is highly dependent on the type of knowledge. We pose the task of predicting properties of concrete nouns in a supervised setting, and compare between learning taxonomic properties (e.g., animacy) and attributive properties (e.g., size, color). We employ four state-of-the-art DMs as sources of feature representation for this task, and show that they all yield poor results when tested on attributive properties, achieving no more than an average F-score of 0.37 in the binary property prediction task, compared to 0.73 on taxonomic properties. Our results suggest that the distributional hypothesis may not be equally applicable to all types of semantic information.
For instance, REF analyzed how well pretrained Skip-gram and GloVe vectors are able to predict properties of nouns, focusing on both taxonomic properties (e.g. being a bird) and attributive properties (e.g. being dangerous).
How Well Do Distributional Models Capture Different Types of Semantic Knowledge?
ACL
Computer Science