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Ribeiro, E., Teixeira, A. S., Ribeiro, R. & Matos, D. M. de. (2020). Semantic frame induction as a community detection problem. In Cherifi, H., Gaito, S., Mendes, J. F., Moro, E. and Rocha, L. M. (Ed.), Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence. (pp. 274-285). Lisboa: Springer.

E. Ribeiro et al.,  "Semantic frame induction as a community detection problem", in Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, Cherifi, H., Gaito, S., Mendes, J. F., Moro, E. and Rocha, L. M., Ed., Lisboa, Springer, 2020, vol. 881, pp. 274-285

@inproceedings{ribeiro2020_1730766161696,
	author = "Ribeiro, E. and Teixeira, A. S. and Ribeiro, R. and Matos, D. M. de.",
	title = "Semantic frame induction as a community detection problem",
	booktitle = "Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence",
	year = "2020",
	editor = "Cherifi, H., Gaito, S., Mendes, J. F., Moro, E. and Rocha, L. M.",
	volume = "881",
	number = "",
	series = "",
	doi = "10.1007/978-3-030-36687-2_23",
	pages = "274-285",
	publisher = "Springer",
	address = "Lisboa",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-030-36687-2"
}

TY  - CPAPER
TI  - Semantic frame induction as a community detection problem
T2  - Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence
VL  - 881
AU  - Ribeiro, E.
AU  - Teixeira, A. S.
AU  - Ribeiro, R.
AU  - Matos, D. M. de.
PY  - 2020
SP  - 274-285
SN  - 1860-949X
DO  - 10.1007/978-3-030-36687-2_23
CY  - Lisboa
UR  - https://link.springer.com/book/10.1007/978-3-030-36687-2
AB  - Resources such as FrameNet provide semantic information that is important for multiple tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances as nodes connected by an edge if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of the SemEval shared task we outperformed all the previous approaches to the task.
ER  -