A Structured Vector Space Model for Hidden Attribute Meaning in Adjective-Noun Phrases
We present an approach to model hid- den attributes in the compositional se- mantics of adjective-noun phrases in a distributional model. For the represen- tation of adjective meanings, we refor- mulate the pattern-based approach for at- tribute learning of Almuhareb (2006) in a structured vector space model (VSM). This model is complemented by a struc- tured vector space representing attribute dimensions of noun meanings. The com- bination of these representations along the lines of compositional semantic principles exposes the underlying semantic relations in adjective-noun phrases. We show that our compositional VSM outperforms sim- ple pattern-based approaches by circum- venting their inherent sparsity problems.