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        <title>Affordance Extraction and Inference based on Semantic Role Labeling</title>
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        <namePart type="given">Daniel</namePart>
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        <namePart type="given">Alípio</namePart>
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            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Cocarascu</namePart>
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    <abstract>Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of ‘coffee’ and ‘tea’ make them similar, or how they could be related to ‘shop’. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.</abstract>
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%0 Conference Proceedings
%T Affordance Extraction and Inference based on Semantic Role Labeling
%A Loureiro, Daniel
%A Jorge, Alípio
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F loureiro-jorge-2018-affordance
%X Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of ‘coffee’ and ‘tea’ make them similar, or how they could be related to ‘shop’. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.
%R 10.18653/v1/W18-5514
%U https://aclanthology.org/W18-5514
%U https://doi.org/10.18653/v1/W18-5514
%P 91-96

Markdown (Informal)

[Affordance Extraction and Inference based on Semantic Role Labeling](https://aclanthology.org/W18-5514) (Loureiro & Jorge, EMNLP 2018)

• Affordance Extraction and Inference based on Semantic Role Labeling (Loureiro & Jorge, EMNLP 2018)

ACL

• Daniel Loureiro and Alípio Jorge. 2018. Affordance Extraction and Inference based on Semantic Role Labeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 91–96, Brussels, Belgium. Association for Computational Linguistics.