Paper: A Discriminative Learning Model for Coordinate Conjunctions

ACL ID D07-1064
Title A Discriminative Learning Model for Coordinate Conjunctions
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We propose a sequence-alignment based method for detecting and disambiguating co- ordinate conjunctions. In this method, av- eraged perceptron learning is used to adapt the substitution matrix to the training data drawn from the target language and domain. To reduce the cost of training data con- struction, our method accepts training exam- ples in which complete word-by-word align- ment labels are missing, but instead only the boundaries of coordinated conjuncts are marked. We report promising empirical re- sults in detecting and disambiguating coor- dinated noun phrases in the GENIA corpus, despite a relatively small number of train- ing examples and minimal features are em- ployed.