Exploring Supervised LDA Models for Assigning Attributes to 
Adjective-Noun Phrases

Matthias Hartung

In this talk, we introduces an attribute selection task as a way to 
characterize the inherent meaning of property-denoting adjectives in 
adjective-noun phrases, such as e.g. "hot" in "hot summer" denoting the 
attribute TEMPERATURE, rather than TASTE. We formulate this task in a 
vector space model that represents adjectives and nouns as vectors in a 
semantic space defined over possible attributes. The vectors incorporate 
latent semantic information obtained from two variants of LDA topic 
models. Our LDA models outperform previous approaches on a small set of 
10 attributes with considerable gains on sparse representations, which 
highlights the strong smoothing power of LDA models. For the first time, 
we extend the attribute selection task to a new data set with more than 
200 classes. We observe that large-scale attribute selection is a hard 
problem, but a subset of attributes performs robustly on the large scale 
as well. Again, the LDA models outperform the VSM baseline.