@InProceedings{akbik-guan-li:2016:COLING,
  author    = {Akbik, Alan  and  Guan, Xinyu  and  Li, Yunyao},
  title     = {Multilingual Aliasing for Auto-Generating Proposition Banks},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3466--3474},
  abstract  = {Semantic Role Labeling (SRL) is the task of identifying the predicate-argument
	structure in sentences with semantic frame and role labels. For the English
	language, the Proposition Bank provides both a lexicon of all possible semantic
	frames and large amounts of labeled training data. In order to expand SRL
	beyond English, previous work investigated automatic approaches based on
	parallel corpora to automatically generate Proposition Banks for new target
	languages (TLs). However, this approach heuristically produces the frame
	lexicon from word alignments, leading to a range of lexicon-level errors and
	inconsistencies. To address these issues, we propose to manually alias TL verbs
	to existing English frames. For instance, the German verb drehen may evoke
	several meanings, including ”turn something” and ”film something”.
	Accordingly, we alias the former to the frame TURN.01 and the latter to a group
	of frames that includes FILM.01 and SHOOT.03. We execute a large-scale manual
	aliasing effort for three target languages and apply the new lexicons to
	automatically generate large Proposition Banks for Chinese, French and German
	with manually curated frames. We present a detailed evaluation in which we find
	that our proposed approach significantly increases the quality and consistency
	of the generated Proposition Banks. We release these resources to the research
	community.},
  url       = {http://aclweb.org/anthology/C16-1327}
}