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A Supervised learning approach for the extraction of opinion sources and targets from German text

  • We present the first systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out specific differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classifier is trained and tested on the dataset of the STEPS shared task.

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Author:Michael Wiegand, Margarita Chikobava, Josef Ruppenhofer
Parent Title (English):Preliminary proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019), October 9 – 11, 2019 at Friedrich-Alexander-Universität Erlangen-Nürnberg
Publisher:German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg
Place of publication:München [u.a.]
Document Type:Conference Proceeding
Year of first Publication:2019
Date of Publication (online):2019/10/15
GND Keyword:Automatische Sprachanalyse; Deutsch; Propositionale Einstellung; Semantische Analyse
First Page:10
Last Page:19
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Program areas:Pragmatik
Program areas:Digitale Sprachwissenschaft
Licence (German):License LogoCreative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International