Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Fourth Learning Language in Logic Workshop |
| Editors | J. Cussens, C. Nedellec |
| Place of Publication | Bonn |
| Publisher | [s.n.] |
| Pages | 3-10 |
| Number of pages | 8 |
| Publication status | Published - 2005 |
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Rule meta-learning for trigram-based sequence processing. / Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.
Proceedings of the Fourth Learning Language in Logic Workshop. ed. / J. Cussens; C. Nedellec. Bonn : [s.n.], 2005. p. 3-10.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
TY - GEN
T1 - Rule meta-learning for trigram-based sequence processing
AU - Canisius, S.V.M.
AU - van den Bosch, A.
AU - Daelemans, W.
N1 - Pagination: 8
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
M3 - Conference contribution
SP - 3
EP - 10
BT - Proceedings of the Fourth Learning Language in Logic Workshop
A2 - Cussens, J.
A2 - Nedellec, C.
PB - [s.n.]
CY - Bonn
ER -