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ReliAble dependency arc recognition
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DOI：10.1016/j.eswa.2013.08.070.png)
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Abstract
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We propose a novel natural language processing task, ReliAble dependency arc recognition (RADAR), which helps high-level applications better utilize the dependency parse trees. We model RADAR as a binary classification problem with imbalanced data, which classifies each dependency parsing arc as correct or incorrect. A logistic regression classifier with appropriate features is trained to recognize reliable dependency arcs (correct with high precision). Experimental results show that the classification method can outperform a probabilistic baseline method, which is calculated by the original graph-based dependency parser. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Natural language processing
Syntactic parsing
Dependency parsing
RADAR
Binary classification
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