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From Kshirsagar et al. (2015)[1]:

"We are optimistic that future improvements to lexical semantic resources, such as crowdsourced lexical expansion of FrameNet (Pavlick et al., 2015) as well as ongoing/planned changes for PropBank (Bonial et al., 2014) and SemLink (Bonial et al., 2013), will lead to further gains in this task. Moreover, the techniques discussed here could be further explored using semi-automatic mappings between lexical resources (such as UBY; Gurevych et al., 2012), and correspondingly, this task could be used to extrinsically validate those mappings. Ours is not the only study to show benefit from heterogeneous annotations for semantic analysis tasks. Feizabadi and Padó (2015), for example, successfully applied similar techniques for SRL of implicit arguments.9 Ultimately, given the diversity of semantic resources, we expect that learning from heterogeneous annotations in different corpora will be necessary to build automatic semantic analyzers that are both accurate and robust."

From Das et al. (2010)[2]:

"A line of work has sought to extend the coverage of FrameNet by exploiting VerbNet, WordNet, and Wikipedia (Shi and Mihalcea, 2005; Giuglea and Moschitti, 2006; Pennacchiotti et al., 2008; Tonelli and Giuliano, 2009), and projecting entries and an- notations within and across languages (Boas, 2002; Fung and Chen, 2004; Pad´ o and Lapata, 2005; Furstenau and Lapata, 2009)."

References Edit

  1. Kshirsagar, M., Thomson, S., Schneider, N., Carbonell, J., Smith, N. A., & Dyer, C. (2015). Frame-Semantic Role Labeling with Heterogeneous Annotations. people, 3, A0.
  2. Das, D., Schneider, N., Chen, D., & Smith, N. A. N. (2010). Probabilistic frame-semantic parsing. HLT ’10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 3(June), 948–956. Retrieved from http://dl.acm.org/citation.cfm?id=1858136\nhttp://dl.acm.org/citation.cfm?id=1857999.1858136