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Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features

  • We present a major step towards the creation of the first high-coverage lexicon of polarity shifters. In this work, we bootstrap a lexicon of verbs by exploiting various linguistic features. Polarity shifters, such as ‘abandon’, are similar to negations (e.g. ‘not’) in that they move the polarity of a phrase towards its inverse, as in ‘abandon all hope’. While there exist lists of negation words, creating comprehensive lists of polarity shifters is far more challenging due to their sheer number. On a sample of manually annotated verbs we examine a variety of linguistic features for this task. Then we build a supervised classifier to increase coverage. We show that this approach drastically reduces the annotation effort while ensuring a high-precision lexicon. We also show that our acquired knowledge of verbal polarity shifters improves phrase-level sentiment analysis.

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Author:Marc Schulder, Michael WiegandGND, Josef RuppenhoferGND, Benjamin Roth
Parent Title (English):Proceedings of the Eighth International Joint Conference on Natural Language Processing, November 27 - December 1, 2017, Taipei, Taiwan (Volume 1: Long Papers)
Publisher:Asian Federation of Natural Language Processing
Place of publication:Taipei
Document Type:Conference Proceeding
Year of first Publication:2017
Date of Publication (online):2019/02/11
GND Keyword:Computerlinguistik; Maschinelles Lernen; Natürliche Sprache; Polarität
First Page:624
Last Page:633
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Program areas:Pragmatik
Licence (English):License LogoCreative Commons - Attribution 4.0 International