id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_s24csddavbdw7gozzjdjmffafy	Silvana Hartmann	Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources	2016	18	.pdf	application/pdf	11541	919	64	Generating Training Data for Semantic Role Labeling based on Label resource-based supervision in relation extraction, we focus on complex linguistic annotations, more specifically FrameNet senses In this work, we present a novel approach to automatically generate training data for semantic role labeling only used WordNet (Cholakov et al., 2014), not considering other sense inventories such as FrameNet. Our distant supervision approach for automatic training data generation employs two types of knowledge sources: LLRs and linguistic knowledge formalized as rules to create data labeled with FrameNet for argument identification and labeling of the semantic roles, which depends on the disambiguation result. training data for SRL consists of two stages, first generating sense-labeled data, then extending these to corpora and evaluate them extrinsically using a classifier trained on the automatically labeled data on In this work, we are the first to apply distant supervision-based verb sense labeling to the FrameNet	./cache/work_s24csddavbdw7gozzjdjmffafy.pdf	./txt/work_s24csddavbdw7gozzjdjmffafy.txt