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    <name type="personal">
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            <title>Proceedings of the 15th International Conference on Parsing Technologies</title>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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            <namePart type="given">Kenji</namePart>
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    <abstract>PP-attachments are an important source of errors in parsing natural language. We propose in this article to use data coming from a multimodal corpus, combining textual, visual and conceptual information, as well as a correction strategy, to propose alternative attachments in the output of a parser.</abstract>
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%0 Conference Proceedings
%T Correcting prepositional phrase attachments using multimodal corpora
%A Delecraz, Sebastien
%A Nasr, Alexis
%A Bechet, Frederic
%A Favre, Benoit
%Y Miyao, Yusuke
%Y Sagae, Kenji
%S Proceedings of the 15th International Conference on Parsing Technologies
%D 2017
%8 September
%I Association for Computational Linguistics
%C Pisa, Italy
%F delecraz-etal-2017-correcting
%X PP-attachments are an important source of errors in parsing natural language. We propose in this article to use data coming from a multimodal corpus, combining textual, visual and conceptual information, as well as a correction strategy, to propose alternative attachments in the output of a parser.
%U https://aclanthology.org/W17-6311/
%P 72-77

Markdown (Informal)

[Correcting prepositional phrase attachments using multimodal corpora](https://aclanthology.org/W17-6311/) (Delecraz et al., IWPT 2017)

• Correcting prepositional phrase attachments using multimodal corpora (Delecraz et al., IWPT 2017)

ACL

• Sebastien Delecraz, Alexis Nasr, Frederic Bechet, and Benoit Favre. 2017. Correcting prepositional phrase attachments using multimodal corpora. In Proceedings of the 15th International Conference on Parsing Technologies, pages 72–77, Pisa, Italy. Association for Computational Linguistics.