Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Kernel methods for relation extraction
The Journal of Machine Learning Research
Semi-automatic recognition of noun modifier relationships
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Building a hyponymy lexicon with hierarchical structure
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
A Google-based statistical acquisition model of Chinese lexical concepts
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Learning concepts from text based on the inner-constructive model
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
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Chinese information processing is a critical step toward cognitive linguistic applications like machine translation. Lexical hyponymy relation, which exists in some Eastern languages like Chinese, is a kind of hyponymy that can be directly inferred from the lexical compositions of concepts, and of great importance in ontology learning. However, a key problem is that the lexical hyponymy is so commonsense that it cannot be discovered by any existing acquisition methods. In this paper, we systematically define lexical hyponymy relationship, its linguistic features and propose a computational approach to semi-automatically learn hierarchical lexical hyponymy relations from a large-scale concept set, instead of analyzing lexical structures of concepts. Our novel approach discovered lexical hyponymy relation by examining statistic features in a Common Suffix Tree. The experimental results show that our approach can correctly discover most lexical hyponymy relations in a given large-scale concept set.