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        <title>Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets</title>
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    <name type="personal">
        <namePart type="given">Gabriel</namePart>
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        <namePart type="given">Judith</namePart>
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    <name type="personal">
        <namePart type="given">Yevgeniy</namePart>
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    <name type="personal">
        <namePart type="given">Iryna</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2056</identifier>
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%0 Conference Proceedings
%T Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
%A Stanovsky, Gabriel
%A Eckle-Kohler, Judith
%A Puzikov, Yevgeniy
%A Dagan, Ido
%A Gurevych, Iryna
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F stanovsky-etal-2017-integrating
%X Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available.
%R 10.18653/v1/P17-2056
%U https://aclanthology.org/P17-2056
%U https://doi.org/10.18653/v1/P17-2056
%P 352-357

Markdown (Informal)

[Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets](https://aclanthology.org/P17-2056) (Stanovsky et al., ACL 2017)

• Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets (Stanovsky et al., ACL 2017)

ACL

• Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, and Iryna Gurevych. 2017. Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 352–357, Vancouver, Canada. Association for Computational Linguistics.