A discriminative approach to tree alignment

Authors:
Jörg Tiedemann;Gideon Kotzé
Affiliations:
Uppsala University, Uppsala/Sweden;Alpha-Informatica, Rijksuniversiteit Groningen, Groningen, The Netherlands
Venue:
MCTLLL '09 Proceedings of the Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning
Year:
2009

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Abstract

In this paper we propose a discriminative framework for automatic tree alignment. We use a rich feature set and a log-linear model trained on small amounts of hand-aligned training data. We include contextual features and link dependencies to improve the results even further. We achieve an overall F-score of almost 80% which is significantly better than other scores reported for this task.