The Journal of Machine Learning Research
A uniform approach to analogies, synonyms, antonyms, and associations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Language models based on semantic composition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A latent dirichlet allocation method for selectional preferences
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Latent variable models of selectional preference
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Assessing the challenge of fine-grained named entity recognition and classification
NEWS '10 Proceedings of the 2010 Named Entities Workshop
A regression model of adjective-noun compositionality in distributional semantics
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A structured vector space model for hidden attribute meaning in adjective-noun phrases
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Exploring supervised lda models for assigning attributes to adjective-noun phrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Exploring supervised lda models for assigning attributes to adjective-noun phrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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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.