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. © 2010 Association for Computational Linguistics.
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|Titolo:||Learning Word-Class Lattices for definition and hypernym extraction|
|Data di pubblicazione:||2010|
|Appartiene alla tipologia:||04b Atto di convegno in volume|