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.
Author: | Michael Wiegand, Margarita Chikobava, Josef Ruppenhofer |
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URN: | urn:nbn:de:bsz:mh39-93218 |
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 |
Program areas: | Pragmatik |
Program areas: | Digitale Sprachwissenschaft |
Licence (German): | ![]() |