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arXiv · 1705.02925

Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Pradeep Dasigi·Waleed Ammar·Chris Dyer·Eduard Hovy
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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.

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Pradeep Dasigi, Waleed Ammar, Chris Dyer, Eduard Hovy. 2017-05-08. Ontology-Aware Token Embeddings for Prepositional Phrase Attachment. https://arxiv.org/abs/1705.02925

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