Exploring Supervised LDA Models for Assigning Attributes to Adjective-Noun Phrases
Matthias Härtung, Anette Frank · PUB – Publications at Bielefeld University (Bielefeld University) · 2011
This paper 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.