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
Paraphrase assessment in structured vector space: exploring parameters and datasets
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Syntactic and semantic factors in processing difficulty: an integrated measure
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A semantic network approach to measuring relatedness
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A flexible, corpus-driven model of regular and inverse selectional preferences
Computational Linguistics
A model of word similarity based on structural alignment of subject-verb-object triples
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a computational model of contextual facilitation based on word co-occurrence vectors, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. The aim of our study is to find out whether special-purpose mechanisms are necessary in order to capture the pattern of the experimental results.