Contextual correlates of synonymy
Communications of the ACM
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Improvements in automatic thesaurus extraction
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Similarity of Semantic Relations
Computational Linguistics
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
A general feature space for automatic verb classification
Natural Language Engineering
Automatically Harvesting and Ontologizing Semantic Relations
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
A uniform approach to analogies, synonyms, antonyms, and associations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Measuring semantic relatedness with vector space models and random walks
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Exemplar-based models for word meaning in context
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Co-related verb argument selectional preferences
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Random indexing distributional semantic models for Croatian language
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Distributional models and lexical semantics in convolution kernels
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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We propose an approach to corpus-based semantics, inspired by cognitive science, in which different semantic tasks are tackled using the same underlying repository of distributional information, collected once and for all from the source corpus. Task-specific semantic spaces are then built on demand from the repository. A straightforward implementation of our proposal achieves state-of-the-art performance on a number of unrelated tasks.