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.

Metadaten
Author:	Michael Wiegand, Margarita Chikobava, Josef Ruppenhofer
URN:	urn:nbn:de:bsz:mh39-93218
URL:	https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/Preliminary_proceedings_of_the_15th_Conference_on_Natural_Language_Processing_KONVENS_2019.pdf
URL:	https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_6.pdf
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
Language:	English
Year of first Publication:	2019
Date of Publication (online):	2019/10/15
Publicationstate:	Veröffentlichungsversion
Reviewstate:	Peer-Review
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
Linguistics-Classification:	Computerlinguistik
Licence (German):	Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International

Open Access