Learning Word-Class Lattices for definition and hypernym extraction
|Learning Word-Class Lattices for definition and hypernym extraction|
|Author(s)||Navigli R., Velardi P.|
|Published in||ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Data sets, Lexico-syntactic patterns, Ontology learning, Question Answering, Recall and precision, Relation extraction, Syntactic structure, Wikipedia, Computational linguistics)|
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Learning Word-Class Lattices for definition and hypernym extraction is a 2010 conference paper written in English by Navigli R., Velardi P. and published in ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference.
Definition extraction is the task of automatically identifying definitional sentences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current approaches - mostly focused on lexicosyntactic patterns - suffer from both low recall and precision, as definitional sentences occur in highly variable syntactic structures. In this paper, we propose Word- Class Lattices (WCLs), a generalization of word lattices that we use to model textual definitions. Lattices are learned from a dataset of definitions from Wikipedia. Our method is applied to the task of definition and hypernym extraction and compares favorably to other pattern generalization methods proposed in the literature.
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