Artificial Intelligence - On connectionist symbol processing
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 bilingual lexicon acquisition using random indexing of parallel corpora
Natural Language Engineering
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The distributional inclusion hypotheses and lexical entailment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Scaling distributional similarity to large corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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
Characterising measures of lexical distributional similarity
COLING '04 Proceedings of the 20th international conference on 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
Supporting inferences in semantic space: representing words as regions
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Towards open-domain Semantic Role Labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
What is word meaning, really?: (and how can distributional models help us describe it?)
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Multiplicity and word sense: evaluating and learning from multiply labeled word sense annotations
Language Resources and Evaluation
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
Connecting the dots: mass, energy, word meaning, and particle-wave duality
QI'12 Proceedings of the 6th international conference on Quantum Interaction
Statistical metaphor processing
Computational Linguistics
| Hi-index | 0.00 |
Vector space models of word meaning typically represent the meaning of a word as a vector computed by summing over all its corpus occurrences. Words close to this point in space can be assumed to be similar to it in meaning. But how far around this point does the region of similar meaning extend? In this paper we discuss two models that represent word meaning as regions in vector space. Both representations can be computed from traditional point representations in vector space. We find that both models perform at over 95% F-score on a token classification task.