PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Assessing interpretable, attribute-related meaning representations for adjective-noun phrases in a similarity prediction task
Matthias Hartung and Anette Frank
In: GEometrical Models of Natural Language Semantics (GEMS-2011), Edinburgh, UK(2011).

Abstract

We present a distributional vector space model that incorporates Latent Dirichlet Allocation in order to capture the semantic relation holding between adjectives and nouns along interpretable dimensions of meaning: The meaning of adjective-noun phrases is characterized in terms of ontological attributes that are prominent in their compositional semantics. The model is evaluated in a similarity prediction task based on paired adjective-noun phrases from the Mitchell and Lapata (2010) benchmark data. Comparing our model against a high-dimensional latent word space, we observe qualitative differences that shed light on different aspects of similarity conveyed by both models and suggest integrating their complementary strengths.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8492
Deposited By:Sebastian Pado
Deposited On:16 February 2012