Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
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We present the UMUS (Université du Maine/Universität Stuttgart) submission for the NEG task at GREC'10. We refined and tuned our 2009 system but we still rely on predicting generic labels and then choosing from the list of expressions that match those labels. We handled recursive expressions with care by generating specific labels for all the possible embeddings. The resulting system performs at a type accuracy of 0.84 an a string accuracy of 0.81 on the development set.