id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_fsexunhxlrhe3giv4462rvneza	Ashish Vaswani	Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States	2016	14	.pdf	application/pdf	8980	987	67	new approach for approximate structured inference for transition-based parsing that produces scores suitable for global scoring using local models. beam search has made transition-based parsing competitive in accuracy (Zhang and Clark, 2008; Huang how effective locally-trained neural network models are at predicting parser actions, while providing parsing approaches employed locally-trained multiclass models to choose a parser action based on the these models, classification is based on a set of features extracted from the current state of the parser, From state 2, the standard way of training local classifiers would be simply to associate features from state 2 to a shift action, of our transition-based parsers are the local classifiers that predict the next action given features derived from the current state. In contrast, Chen and Manning (2014) use 48 feature templates, including higher-order dependency information than has been shown to improve parsing accuracy significantly (Zhang and Nivre, 2011).	./cache/work_fsexunhxlrhe3giv4462rvneza.pdf	./txt/work_fsexunhxlrhe3giv4462rvneza.txt