Sparse Distributed Memory
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
UNIBA: distributional semantics for textual similarity
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
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Distributional approaches are based on a simple hypothesis: the meaning of a word can be inferred from its usage. The application of that idea to the vector space model makes possible the construction of a WordSpace in which words are represented by mathematical points in a geometric space. Similar words are represented close in this space and the definition of "word usage" depends on the definition of the context used to build the space, which can be the whole document, the sentence in which the word occurs, a fixed window of words, or a specific syntactic context. However, in its original formulation WordSpace can take into account only one definition of context at a time. We propose an approach based on vector permutation and Random Indexing to encode several syntactic contexts in a single WordSpace. Moreover, we propose some operations in this space and report the results of an evaluation performed using the GEMS 2011 Shared Evaluation data.