Abstract
Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a dependency chain. The effect of these alternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary subtrees of dependency trees. We describe a discovery procedure for this model and demonstrate experimentally an improvement in recall using Subtree patterns.
Original language | English (US) |
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Journal | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
Volume | 2003-July |
State | Published - 2003 |
Event | 41st Annual Meeting of the Association for Computational Linguistics, ACL 2003 - Sapporo, Japan Duration: Jul 7 2003 → Jul 12 2003 |
ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics