Foundations of statistical natural language processing
Foundations of statistical natural language processing
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Language models based on semantic composition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Unsupervised classification with dependency based word spaces
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Exploring supervised lda models for assigning attributes to adjective-noun phrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Finding additional semantic entity information for search engines
Proceedings of the Seventeenth Australasian Document Computing Symposium
WebChild: harvesting and organizing commonsense knowledge from the web
Proceedings of the 7th ACM international conference on Web search and data mining
Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet
Language Resources and Evaluation
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We present an approach to model hidden attributes in the compositional semantics of adjective-noun phrases in a distributional model. For the representation of adjective meanings, we reformulate the pattern-based approach for attribute learning of Almuhareb (2006) in a structured vector space model (VSM). This model is complemented by a structured vector space representing attribute dimensions of noun meanings. The combination of these representations along the lines of compositional semantic principles exposes the underlying semantic relations in adjective-noun phrases. We show that our compositional VSM outperforms simple pattern-based approaches by circumventing their inherent sparsity problems.