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

ACL ID D11-1050
Title Exploring Supervised LDA Models for Assigning Attributes to Adjective-Noun Phrases
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

This paper introduces an attribute selection task as a way to characterize the inherent mea- ning of property-denoting adjectives in adjec- tive-noun phrases, such as e.g. hot in hot sum- mer denoting the attribute TEMPERATURE, rather than TASTE. We formulate this task in a vector space model that represents adjec- tives and nouns as vectors in a semantic space defined over possible attributes. The vectors incorporate latent semantic information ob- tained from two variants of LDA topic mod- els. Our LDA models outperform previous ap- proaches 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 ...