Rule meta-learning for trigram-based sequence processing
Canisius,S.V.M. van den Bosch,A. Daelemans,W.
Canisius,S.V.M.
van den Bosch,A.
Daelemans,W.
Abstract
Predicting overlapping trigrams of class labels is a recently-proposed method to improve performance on sequence labelling tasks. In this method, sequence elements are effectively classified three times, therefore some procedure is needed to post-process those overlapping classifications into one output sequence. In this paper, we present a rule-based procedure learned automatically from training data. In combination with a memory-based leaner predicting class trigrams, the performance of this meta-learned overlapping trigram post-processor matches that of a handcrafted post-processing rule used in the original study on class trigrams. Moreover, on two domain-specific entity chunking tasks, the class trigram method with automatically learned post-processing rules compares favourably with recent probabilistic sequence labelling techniques, such as maximum-entropy markov models and conditional random fields.
Description
Pagination: 8
Date
2005
Journal Title
Journal ISSN
Volume Title
Publisher
[s.n.]
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Canisius, S V M, van den Bosch, A & Daelemans, W 2005, Rule meta-learning for trigram-based sequence processing. in J Cussens & C Nedellec (eds), Proceedings of the Fourth Learning Language in Logic Workshop. [s.n.], Bonn, pp. 3-10.
License
info:eu-repo/semantics/restrictedAccess