Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
A general framework for distributional similarity
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
The distributional inclusion hypotheses and lexical entailment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
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
Representing words as regions in vector space
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Learning entailment rules for unary templates
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Bootstrapping distributional feature vector quality
Computational Linguistics
Supporting inferences in semantic space: representing words as regions
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Context-theoretic semantics for natural language: an overview
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Directional distributional similarity for lexical inference
Natural Language Engineering
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
How we BLESSed distributional semantic evaluation
GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Hi-index | 0.00 |
In this paper we apply existing directional similarity measures to identify hypernyms with a state-of-the-art distributional semantic model. We also propose a new directional measure that achieves the best performance in hypernym identification.