APA
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Cetto, M., Niklaus, C., Freitas, A., & Handschuh, S. (2018). Graphene: Semantically-Linked Propositions in Open Information Extraction. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 2300–2311). Santa Fe, New Mexico, USA: Association for Computational Linguistics.
Chicago/Turabian
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Cetto, Matthias, Christina Niklaus, André Freitas, and Siegfried Handschuh. “Graphene: Semantically-Linked Propositions in Open Information Extraction.” In Proceedings of the 27th International Conference on Computational Linguistics, 2300–2311. Santa Fe, New Mexico, USA: Association for Computational Linguistics, 2018.
MLA
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Cetto, Matthias, et al. “Graphene: Semantically-Linked Propositions in Open Information Extraction.” Proceedings of the 27th International Conference on Computational Linguistics, Association for Computational Linguistics, 2018, pp. 2300–11.
BibTeX Click to copy
@inproceedings{cetto2018a,
title = {Graphene: Semantically-Linked Propositions in Open Information Extraction},
year = {2018},
month = aug,
address = {Santa Fe, New Mexico, USA},
pages = {2300--2311},
publisher = {Association for Computational Linguistics},
author = {Cetto, Matthias and Niklaus, Christina and Freitas, André and Handschuh, Siegfried},
booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
month_numeric = {8}
}
We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.
