A Discriminative Learning Model for Coordinate Conjunctions

Masashi Shimbo, Kazuo Hara

Abstract: We propose a sequence-alignment based method for detecting and disambiguatingcoordinate conjunctions. In this method, averaged 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 construction, our method accepts training examples in which complete word-by-word alignment labels are missing, but instead only the boundaries of coordinated conjuncts are marked. We report promising empirical results in detecting and disambiguating coordinated noun phrases in the GENIA corpus, despite a relatively small number of training examples and minimal features are employed.