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        <title>Ontology-Aware Token Embeddings for Prepositional Phrase Attachment</title>
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        <namePart type="given">Pradeep</namePart>
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            <namePart type="family">Barzilay</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.</abstract>
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%0 Conference Proceedings
%T Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
%A Dasigi, Pradeep
%A Ammar, Waleed
%A Dyer, Chris
%A Hovy, Eduard
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F dasigi-etal-2017-ontology
%X Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.
%R 10.18653/v1/P17-1191
%U https://aclanthology.org/P17-1191
%U https://doi.org/10.18653/v1/P17-1191
%P 2089-2098

Markdown (Informal)

[Ontology-Aware Token Embeddings for Prepositional Phrase Attachment](https://aclanthology.org/P17-1191) (Dasigi et al., ACL 2017)

• Ontology-Aware Token Embeddings for Prepositional Phrase Attachment (Dasigi et al., ACL 2017)

ACL

• Pradeep Dasigi, Waleed Ammar, Chris Dyer, and Eduard Hovy. 2017. Ontology-Aware Token Embeddings for Prepositional Phrase Attachment. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2089–2098, Vancouver, Canada. Association for Computational Linguistics.