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Visual Disambiguation of Preprositional Phrase Attachments : Multimodal Machine Learning for Syntactic Analysis Correction

Abstract : Prepositional phrase attachments are known to be an important source of errors in parsing natural language. In some cases, pure syntactic features cannot be used for prepositional phrase attachment disambiguation while visual features could help. In this work, we are interested in the impact of the integration of such features in a parsing system. We propose a correction strategy pipeline for prepositional attachments using visual information, trained on a multimodal corpus of images and captions. The evaluation of the system shows us that using visual features allows, in certain cases, to correct the errors of a parser. It also helps to identify the most difficult aspects of such integration.
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https://hal.archives-ouvertes.fr/hal-02465051
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Submitted on : Friday, April 10, 2020 - 10:39:08 AM
Last modification on : Wednesday, November 3, 2021 - 6:44:54 AM

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Sebastien Delecraz, Leonor Becerra-Bonache, Alexis Nasr, Frédéric Bechet, Benoit Favre. Visual Disambiguation of Preprositional Phrase Attachments : Multimodal Machine Learning for Syntactic Analysis Correction. IWANN: International Work-Conference on Artificial Neural Networks, Jun 2019, Gran Canaria, Spain. ⟨10.1007/978-3-030-20521-8_52⟩. ⟨hal-02465051⟩

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