Unsupervised methods for developing taxonomies by combining syntactic and statistical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
A metric-based framework for automatic taxonomy induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A semi-supervised method to learn and construct taxonomies using the web
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Evaluation method for automated wordnet expansion
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
A graph-based algorithm for inducing lexical taxonomies from scratch
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hi-index | 0.00 |
The paper presents an algorithm of the automated wordnet expansion which can utilise results produced by both pattern-based and Distributional Similarity methods. It is based on the assumption that all relation extraction methods express some error, so we cannot identify the exact place (synset) for a new lemma on their bases, but an area (a wordnet subgraph). Support for a particular attachment point generated by knowledge should be expanded on the surrounding synsets. Moreover, the wordnet structure is modelled on the level of links between lexical units. Evaluation of the algorithm and comparison with top algorithm from literature in large scale experiments on Princeton WordNet is presented.