DFKI-LT - Boosting Relation Extraction with Limited Closed-World Knowledge
Boosting Relation Extraction with Limited Closed-World Knowledge
4 Proceedings of the 23rd International Conference on Computational Linguistics, Poster Session, Beijing, China, o.A., Association for Computational Linguistics, 2010,
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation of learned rules to the usual seed data, the precision of relation extraction can be considerably improved. Starting from an existing baseline system we demonstrate that utilizing limited closed world knowledge can effectively eliminate "dangerous" or plainly wrong rules during the bootstrapping process. The new method improves the reliability of the confidence estimation and the precision value of the extracted instances. Although recall suffers to a certain degree depending on the domain and the selected settings, the overall performance measured by F-score considerably improves. Finally we validate the adaptability of the best ranking method to a new domain and obtain promising results.
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