@InProceedings{qasemizadeh-EtAl:2019:S19-2,
  author    = {QasemiZadeh, Behrang  and  Petruck, Miriam R L  and  Stodden, Regina  and  Kallmeyer, Laura  and  Candito, Marie},
  title     = {SemEval-2019 Task 2: Unsupervised Lexical Frame Induction},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {16--30},
  abstract  = {This paper presents Unsupervised LexicalFrame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019.Given a set of prespecified subcategorization frames in context, the task requires that words and their arguments be clustered such that they resemble semantic frame structures. Results are useful in identifying words of indistinguishable lexical frame structures and discerning semantic relations of the arguments. The evaluation of unsupervised frame methods fell into two tracks: 1)Verb Clustering based onFrameNet 1.7; and 2)Argument Clustering based on: a) FrameNet core frame elements, and b) VerbNet 3.2 semantic roles. The shared task attracted 13 participants, of which three reported promising results. This work reports on the methods and resources that these systems employed, and includes a comparison to human annotation.},
  url       = {http://www.aclweb.org/anthology/S19-2003}
}