The Excitement Open Platform for Textual Inferences

By Bernardo Magnini, Roberto Zanoli, Ido Dagan, Kathrin Eichler, Günter Neumann, Tae-Gil Noh, Sebastian Padó, Asher Stern, Omer Levy. 2014

72 citations recorded by the source corpus.

This paper presents the Excitement Open Platform (EOP), a generic architecture and a comprehensive implementation for textual inference in multiple languages. The platform includes state-of-art algorithms, a large number of knowledge resources, and facilities for experimenting and testing innovative approaches. The EOP is distributed as an open source software.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A large annotated corpus for learning natural language inference (2015, background): “The ﬁrst class of models is from the Excitement Open Platform (EOP, Pad´o et al. 2014; Magnini et al. 2014)—an open source platform for RTE research.”
SciTaiL: A Textual Entailment Dataset from Science Question Answering (2018, methodology): “com/lukecq1231/nli (13)stemmed and stop-word filtered (14)MaxEntropy-based model from the Excitement Platform (Magnini et al. 2014) also achieved similar scores on this dataset.”
Emergent: a novel data-set for stance classification (2016, result): “We also compare our classiﬁer against the Excitement Open Platform (EOP) textual entailment classiﬁer (Magnini et al., 2014).”
Most “babies” are “little” and most “problems” are “huge”: Compositional Entailment in Adjective-Nouns (2016, methodology): “(a) Systems from Magnini et al. (2014) on RTE3.”