Abstract

Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ such representations yield modest gains in PP attachment accuracy. In this paper, we show that word vector representations can yield significant PP attachment performance gains. This is achieved via a non-linear architecture that is discriminatively trained to maximize PP attachment accuracy. The architecture is initialized with word vectors trained from unlabeled data, and relearns those to maximize attachment accuracy. We obtain additional performance gains with alternative representations such as dependency-based word vectors. When tested on both English and Arabic datasets, our method outperforms both a strong SVM classifier and state-of-the-art parsers. For instance, we achieve 82.6% PP attachment accuracy on Arabic, while the Turbo and Charniak self-trained parsers obtain 76.7% and 80.8% respectively.

Highlights

  • The problem of prepositional phrase (PP) attachment disambiguation has been under investigation

  • We show that embedding-based representations can significantly improve PP attachment accuracy

  • We show that word vectors, induced from large volumes of raw data, yield significant PP attachment performance gains

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Summary

Introduction

The problem of prepositional phrase (PP) attachment disambiguation has been under investigationShe ate spaghetti with butterShe ate spaghetti with chopsticks for a long time. The problem of prepositional phrase (PP) attachment disambiguation has been under investigation. Despite at least two decades of research (Brill and Resnik, 1994; Ratnaparkhi et al, 1994; Collins and Brooks, 1995), it remains a major source of errors for state-of-the-art parsers. The extent of improvement over time has been rather limited, amounting to about 32% error reduction since the work of (Collins, 1997). This word determines the structure of the whole PP attachment.

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