Publikation
Boosting Relation Extraction with Limited Closed-World Knowledge
Feiyu Xu; Hans Uszkoreit; Sebastian Krause; Hong Li
In: Proceedings of the 23rd International Conference on Computational Linguistics, Poster Session. International Conference on Computational Linguistics (COLING-2010), 23rd, August 23-27, Beijing, China, 2010.
Zusammenfassung
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.