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(Schlagwörter: "Rhetorik")

Form und Inhalt. Über die Erziehungsvorstellungen der Neuen Rechten in Gestalt von Sommerfelds Buch […] Jornitz, Sieglinde Journal Article | In: Pädagogische Korrespondenz | 2020 40717 Endnote: Author(s): Jornitz, Sieglinde
Title: Form und Inhalt. Über die Erziehungsvorstellungen der Neuen Rechten in Gestalt von Sommerfelds Buch "Wir erziehen"
In: Pädagogische Korrespondenz, (2020) 61, S. 33-50
URN: urn:nbn:de:0111-pedocs-205908
URL: https://nbn-resolving.org/urn:nbn:de:0111-pedocs-205908
Publication Type: 3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language: Deutsch
Keywords: Erziehung; Neue Rechte; Reformpädagogik; Rhetorik
Abstract: Die Autorin analysiert das Buch von Caroline Sommerfeld "Wir erziehen", in dem die Erziehungsvorstellungen der Neuen Rechten dargelegt werden. Die Autorin zeigt in drei Aspekten, wie argumentiert wird. Es geht dabei um die Inszenierung eines Wir, das allen anderen gegenüber gestellt wird; um die Schaffung einer historischen Kontinuität in pädagogischer Hinsicht sowie um die Re-Aktualisierung von Begrifflichkeiten, die u.a. durch den Nationalsozialismus nicht mehr ungebrochen genutzt werden können - auch weil sich Erziehungsvorstellungen wandeln.
Abstract (english): The author is analysing the book "Wir erziehen" of the German and Austrian-based Caroline Sommerfeld, one of the leading persons of the new right wing movement "Die Identitären". By focussing on three aspects, the rhetoric of the book is described. Sommerfeld creates a We that she opposes a societal majority; she creates a historical continuitiy of pedagogy of the 1930s up to the present and she uses educational terms that fail during the regime of the fascists. Sieglinde Jornitz can show that Sommerfeld has not written a concise theory of educational practice but a text that gambles with the fascination of right wing rhetoric and terms.
DIPF-Departments: Informationszentrum Bildung

Neural end-to-end learning for computational argumentation mining Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017 37878 Endnote: Author(s): Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna
Title: Neural end-to-end learning for computational argumentation mining
In: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 11-22
DOI: 10.18653/v1/P17-1002
URL: https://aclanthology.info/pdf/P/P17/P17-1002.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Argumentation; Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Rhetorik; Semantik; Textanalyse
Abstract: We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiL-STMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

SemEval-2017 task 7. Detection and interpretation of English puns Miller, Tristan; Hempelmann, Christian; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the workshop, August 3-4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017 37877 Endnote: Author(s): Miller, Tristan; Hempelmann, Christian; Gurevych, Iryna
Title: SemEval-2017 task 7. Detection and interpretation of English puns
In: Association for Computational Linguistics (Hrsg.): 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the workshop, August 3-4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 58-68
DOI: 10.18653/v1/S17-2005
URL: http://aclweb.org/anthology/S17-2005
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Computerlinguistik; Wort; Humor; Rhetorik; Semantik; Linguistik; Phonologie; Automatisierung; Erkennen; Interpretation; System; Evaluation
Abstract: A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-per-context assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems' methodologies, resources, and results. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using […] Habernal, Ivan; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers | Stroudsburg; PA: Association for Computational Linguistics | 2016 36970 Endnote: Author(s): Habernal, Ivan; Gurevych, Iryna
Title: Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 1589-1599
URL: http://www.aclweb.org/anthology/P16-1150
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Algorithmus; Argumentation; Automatisierung; Computerlinguistik; Kommunikation; Online; Prognose; Qualität; Rhetorik; Soziale Software; Textanalyse; Überzeugung; World wide web 2.0
Abstract (english): We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation "A is more convincing than B" exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman's correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung