Text classification using string kernels
The Journal of Machine Learning Research
The distributional inclusion hypotheses and lexical entailment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Characterising measures of lexical distributional similarity
COLING '04 Proceedings of the 20th international conference on 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
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A probabilistic setting and lexical cooccurrence model for textual entailment
EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Measuring distributional similarity in context
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Directional distributional similarity for lexical inference
Natural Language Engineering
Unsupervised entailment detection between dependency graph fragments
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Identifying hypernyms in distributional semantic spaces
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Terminological paraphrase extraction from scientific literature based on predicate argument tuples
Journal of Information Science
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We present the context-theoretic framework, which provides a set of rules for the nature of composition of meaning based on the philosophy of meaning as context. Principally, in the framework the composition of the meaning of words can be represented as multiplication of their representative vectors, where multiplication is distributive with respect to the vector space. We discuss the applicability of the framework to a range of techniques in natural language processing, including subsequence matching, the lexical entailment model of Dagan et al. (2005), vector-based representations of taxonomies, statistical parsing and the representation of uncertainty in logical semantics.