Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Principle-based parsing without overgeneration
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic bilingual lexicon acquisition using random indexing of parallel corpora
Natural Language Engineering
Semantic taxonomy induction from heterogenous evidence
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
Introduction to Information Retrieval
Introduction to Information Retrieval
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Representing words as regions in vector space
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Entailment above the word level in distributional semantics
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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
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Semantic space models represent the meaning of a word as a vector in high-dimensional space. They offer a framework in which the meaning representation of a word can be computed from its context, but the question remains how they support inferences. While there has been some work on paraphrase-based inferences in semantic space, it is not clear how semantic space models would support inferences involving hyponymy, like horse ran → animal moved. In this paper, we first discuss what a point in semantic space stands for, contrasting semantic space with Gärdenforsian conceptual space. Building on this, we propose an extension of the semantic space representation from a point to a region. We present a model for learning a region representation for word meaning in semantic space, based on the fact that points at close distance tend to represent similar meanings. We show that this model can be used to predict, with high precision, when a hyponymy-based inference rule is applicable. Moving beyond paraphrase-based and hyponymy-based inference rules, we last discuss in what way semantic space models can support inferences.