Coordination Boundary Identification with Similarity and Replaceability
نویسندگان
چکیده
We propose a neural network model for coordination boundary detection. Our method relies on two common properties — similarity and replaceability in conjuncts — in order to detect both similar and dissimilar pairs of conjuncts. The model improves the identification of clause-level coordination using bidirectional recurrent neural networks incorporating two properties as features. We show that our model outperforms existing stateof-the-art methods for the coordination annotated Penn Treebank and Genia corpus without any syntactic information from parsers.
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