A Discriminative Approach to Tree Alignment
نویسندگان
چکیده
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
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