id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ckevfqmvd5eidkjrn3rfy5b4eq	Wanxiang Che	ReliAble dependency arc recognition	2014	7	.pdf	application/pdf	5994	704	57	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 A logistic regression classifier with appropriate features is trained to recognize reliable can outperform a probabilistic baseline method, which is calculated by the original graph-based dependency parser. aims to find a dependency parse tree among words for a sentence. Fig. 1 shows an example of dependency parse tree for a sentence, a classifier, the classification method includes three sorts of features and a process to construct training data. The most intuitive method is to use the probability of a dependency arc to denote its reliability. based on different learning schemes agree on one dependency arc, it will probably be a reliable one. Number of sentences and dependency arcs in training, development, and test set and	./cache/work_ckevfqmvd5eidkjrn3rfy5b4eq.pdf	./txt/work_ckevfqmvd5eidkjrn3rfy5b4eq.txt