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        <title>ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for “zero-shot” open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.</abstract>
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%0 Conference Proceedings
%T ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
%A Lockard, Colin
%A Shiralkar, Prashant
%A Dong, Xin Luna
%A Hajishirzi, Hannaneh
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lockard-etal-2020-zeroshotceres
%X In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for “zero-shot” open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.
%R 10.18653/v1/2020.acl-main.721
%U https://aclanthology.org/2020.acl-main.721/
%U https://doi.org/10.18653/v1/2020.acl-main.721
%P 8105-8117

Markdown (Informal)

[ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages](https://aclanthology.org/2020.acl-main.721/) (Lockard et al., ACL 2020)

• ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages (Lockard et al., ACL 2020)

ACL

• Colin Lockard, Prashant Shiralkar, Xin Luna Dong, and Hannaneh Hajishirzi. 2020. ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8105–8117, Online. Association for Computational Linguistics.