A Discourse Commitment-Based Framework for Recognizing Textual Entailment

By Andrew Hickl, Jeremy Bensley. 2007

94 citations recorded by the source corpus.

In this paper, we introduce a new framework for recognizing textual entailment which depends on extraction of the set of publicly-held beliefs -- known as discourse commitments -- that can be ascribed to the author of a text or a hypothesis. Once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. Promising results were achieved: our system correctly identified more than 80% of examples from the RTE-3 Test Set correctly, without the need for additional sources of training data or other web-based resources.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Computational Linguistics & Semantics

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Citations in the corpus

Combining Natural Logic and Shallow Reasoning for Question Answering (2016, background): “The discourse commitments in Hickl and Bensley (2007) can be thought of as similar to the additional entailed facts we add to the knowledge base (Section 3.3).”
FactBank: a corpus annotated with event factuality (2009, background): “Most significantly, the system that obtained the best absolute result in the three RTE challenges, scoring an 80% accuracy ( Hickl and Bensley 2007 ), is based on identifying the set of publicly-expressed beliefs of the author; that is, on the author’s commitments of how things are in the world acco”
Semantic Annotation for Textual Entailment Recognition (2012, background): “38% LCC [17] 80% Ca Foscari Venice [11] 60.”