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            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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    <abstract>This paper applies contextualized word embedding models to a long-standing problem in the natural language parsing community, namely prepositional phrase attachment. Following past formulations of this problem, we use data sets in which the attachment decision is both a binary-valued choice as well as a multi-valued choice. We present a deep learning architecture that fine-tunes the output of a contextualized word embedding model for the purpose of predicting attachment decisions. We present experiments on two commonly used datasets that outperform the previous best results, using only the original training data and the unannotated full sentence context.</abstract>
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%0 Conference Proceedings
%T Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings
%A Ratnaparkhi, Adwait
%A Kumar, Atul
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F ratnaparkhi-kumar-2021-resolving
%X This paper applies contextualized word embedding models to a long-standing problem in the natural language parsing community, namely prepositional phrase attachment. Following past formulations of this problem, we use data sets in which the attachment decision is both a binary-valued choice as well as a multi-valued choice. We present a deep learning architecture that fine-tunes the output of a contextualized word embedding model for the purpose of predicting attachment decisions. We present experiments on two commonly used datasets that outperform the previous best results, using only the original training data and the unannotated full sentence context.
%U https://aclanthology.org/2021.icon-main.40
%P 335-340

Markdown (Informal)

[Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings](https://aclanthology.org/2021.icon-main.40) (Ratnaparkhi & Kumar, ICON 2021)

• Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings (Ratnaparkhi & Kumar, ICON 2021)

ACL

• Adwait Ratnaparkhi and Atul Kumar. 2021. Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 335–340, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).