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  1. A descriptive analysis of a German corpus annotated with opinion sources and targets
    Autor*in: Wiegand, Michael
    Erschienen: 2019
    Verlag:  Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, Mannheim

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    Quelle: DNB Sachgruppe Deutsche Sprache und Literatur
    Beteiligt: Lapp, Leonie (Verfasser); Ruppenhofer, Josef (Verfasser)
    Sprache: Englisch
    Medientyp: Unbestimmt
    Format: Online
    Weitere Identifier:
    Schlagworte: Deutsch; Politische Sprache; Gesprochene Sprache; Propositionale Einstellung; Automatische Sprachanalyse
    Umfang: Online-Ressource
    Bemerkung(en):

    In: Preliminary proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019), October 9 – 11, 2019 at Friedrich-Alexander-Universität Erlangen-Nürnberg. - München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019., S. 20-29
  2. A descriptive analysis of a German corpus annotated with opinion sources and targets
    Autor*in: Wiegand, Michael ; Lapp, Leonie ; Ruppenhofer, Josef
    Erschienen: 2019

  3. Inducing a Lexicon of Abusive Words – a Feature-Based Approach
    Autor*in: Wiegand, Michael ; Ruppenhofer, Josef ; Schmidt, Anna ; Greenberg, Clayton
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small... mehr

     

    We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the crossdomain detection of abusive microposts.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Text Mining; Beleidigung; Natürliche Sprache
    Lizenz:

    creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
  4. Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features
    Autor*in: Schulder, Marc ; Wiegand, Michael ; Ruppenhofer, Josef ; Roth, Benjamin
    Erschienen: 2019
    Verlag:  Taipei : Asian Federation of Natural Language Processing

    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... mehr

     

    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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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Polarität; Natürliche Sprache; Maschinelles Lernen
    Lizenz:

    creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
  5. Proceedings of GermEval 2018, 14th Conference on Natural Language Processing (KONVENS 2018), Vienna, Austria – September 21, 2018
    Autor*in: Ruppenhofer, Josef ; Siegel, Melanie ; Wiegand, Michael
    Erschienen: 2019
    Verlag:  Vienna, Austria : Austrian Academy of Sciences

    Offensive language in social media is a problem currently widely discussed. Researchers in language technology have started to work on solutions to support the classification of offensive posts. We present the pilot edition of the GermEval Shared... mehr

     

    Offensive language in social media is a problem currently widely discussed. Researchers in language technology have started to work on solutions to support the classification of offensive posts. We present the pilot edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. GermEval 2018 is the fourth workshop in a series of shared tasks on German processing.

     
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      BibTeX-Format
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    DDC Klassifikation: Germanische Sprachen; Deutsch (430)
    Schlagworte: Social Media; Beleidigung; Deutsch; Automatische Sprachverarbeitung; Twitter; Kongress
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  6. Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language
    Autor*in: Wiegand, Michael ; Siegel, Melanie ; Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  Vienna, Austria : Austrian Academy of Sciences

    We present the pilot edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. It comprises two tasks, a coarse-grained binary classification task... mehr

     

    We present the pilot edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. It comprises two tasks, a coarse-grained binary classification task and a fine-grained multi-class classification task. The shared task had 20 participants submitting 51 runs for the coarse-grained task and 25 runs for the fine-grained task. Since this is a pilot task, we describe the process of extracting the raw-data for the data collection and the annotation schema. We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at projects.cai. fbi.h-da.de/iggsa/

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Aufsatz aus einem Sammelband
    Format: Online
    DDC Klassifikation: Germanische Sprachen; Deutsch (430)
    Schlagworte: Social Media; Twitter; Beleidigung; Deutsch; Automatische Sprachverarbeitung
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  7. The treatment of emotion vocabulary in FrameNet: past, present and future developments
    Autor*in: Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  Düsseldorf : düsseldorf university press

    Both for psychology and linguistics, emotion concepts are a continuing challenge for analysis in several respects. In this contribution, we take up the language of emotion as an object of study from several angles. First, we consider how frame... mehr

     

    Both for psychology and linguistics, emotion concepts are a continuing challenge for analysis in several respects. In this contribution, we take up the language of emotion as an object of study from several angles. First, we consider how frame semantic analyses of this domain by the FrameNet project have been developing over time, due to theory-internal as well as application-oriented goals, towards ever more fine-grained distinctions and greater within-frame consistency. Second, we compare how FrameNet’s linguistically oriented analysis of lexical items in the emotion domain compares to the analysis by domain experts of the experiences that give rise (directly or indirectly) to the lexical items. And finally, we consider to what extent frame semantic analysis can capture phenomena such as connotation and inference about attitudes, which are important in the field of sentiment analysis and opinion mining, even if they do not involve the direct evocation of emotion.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Germanische Sprachen; Deutsch (430)
    Schlagworte: Frame-Semantik; Semantische Analyse; Automatische Sprachverarbeitung
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  8. Automatically creating a lexicon of verbal polarity shifters: mono- and cross-lingual methods for German
    Autor*in: Schulder, Marc ; Wiegand, Michael ; Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  Stroudsburg PA, USA : The Association for Computational Linguistics

    In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon... mehr

     

    In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon all hope”. This is similar to how negation words like “not” can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available. ; In dieser Arbeit untersuchen wir Methoden zur Erstellung eines deutschsprachigen Lexikons polaritätsverschiebender Verben. Diese Verben, die vielfach auch Polaritätsshifter genannt werden, sind Inhaltswörter, die die Polarität einer Phrase zu ihrem entgegengesetzten Wert verschieben, wie z.B. das Verb „aufgeben“ in der Verbalphrase „alle Hoffnung aufgeben“. Das Verhalten von Polaritätsshiftern ähnelt somit dem von Negationswörtern wie „nicht“. Für robuste Sentimentanalyse werden sowohl Negationswörter als auch Polaritätsshifter benötigt. Während Listen von Negationswörtern in vielen Sprachen verfügbar sind, existiert jedoch ein Polaritätsshifter-Lexikon hinreichender Größe nur für das Englische. Jene Ressource wurde mittels Bootstrapping erzeugt, indem ein überwachter Klassifikator auf einer kleinen Stichprobe von Verben ...

     
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      BibTeX-Format
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Aufsatz aus einem Sammelband
    Format: Online
    DDC Klassifikation: Germanische Sprachen; Deutsch (430)
    Schlagworte: Semantische Analyse; Verb; Polaritätsprofil; Wortliste; Automatische Sprachverarbeitung
    Lizenz:

    creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
  9. Distinguishing affixoid formations from compounds
    Autor*in: Ruppenhofer, Josef ; Wiegand, Michael ; Wilm, Rebecca ; Markert, Katja
    Erschienen: 2019
    Verlag:  Stroudsburg PA, USA : The Association for Computational Linguistics

    We study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them – increased productivity; a bleached semantics, which is often evaluative and/or intensifying and thus of relevance to... mehr

     

    We study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them – increased productivity; a bleached semantics, which is often evaluative and/or intensifying and thus of relevance to sentiment analysis; and the existence of a free morpheme counterpart – but not been validated empirically. In experiments on a new data set that we make available, we put these key assumptions from the morphological literature to the test and show that despite the fact that affixoids generate many low-frequency formations, we can classify these as affixoid or non-affixoid instances with a best F1-score of 74%.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Aufsatz aus einem Sammelband
    Format: Online
    DDC Klassifikation: Germanische Sprachen; Deutsch (430)
    Lizenz:

    creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
  10. Evaluating the Morphological Compositionality of Polarity
    Autor*in: Ruppenhofer, Josef ; Steiner, Petra ; Wiegand, Michael
    Erschienen: 2019
    Verlag:  Shoumen : Incoma Ltd.

    Unknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very... mehr

     

    Unknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Natürliche Sprache; Computerlinguistik; Polarität; Text Mining; Automatische Sprachverarbeitung; semantische Analyse
    Lizenz:

    creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
  11. Detection of abusive language: the problem of biased datasets
    Autor*in: Wiegand, Michael ; Ruppenhofer, Josef ; Kleinbauer, Thomas
    Erschienen: 2019
    Verlag:  Stroudsburg, PA, USA : The Association for Computational Linguistics

    We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably... mehr

     

    We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Schimpfwort; Beleidigung; Verbalagression; Automatische Sprachanalyse
    Lizenz:

    creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess
  12. Detecting derogatory compounds – an unsupervised approach
    Autor*in: Wiegand, Michael ; Wolf, Maximilian ; Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  Stroudsburg, PA, USA : The Association for Computational Linguistics

    We examine the new task of detecting derogatory compounds (e.g. curry muncher). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. idiot) since they are more sparsely represented in lexical resources previously... mehr

     

    We examine the new task of detecting derogatory compounds (e.g. curry muncher). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. idiot) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Kompositum <Wortbildung>; Schimpfwort; Automatische Sprachanalyse
    Lizenz:

    creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess
  13. Not my president: how names and titles frame political figures
    Autor*in: van den Berg, Esther ; Korfhage, Katharina ; Wiegand, Michael ; Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  Stroudsburg, PA, USA : The Association for Computational Linguistics

    Naming and titling have been discussed in sociolinguistics as markers of status or solidarity. However, these functions have not been studied on a larger scale or for social media data. We collect a corpus of tweets mentioning presidents of six G20... mehr

     

    Naming and titling have been discussed in sociolinguistics as markers of status or solidarity. However, these functions have not been studied on a larger scale or for social media data. We collect a corpus of tweets mentioning presidents of six G20 countries by various naming forms. We show that naming variation relates to stance towards the president in a way that is suggestive of a framing effect mediated by respectfulness. This confirms sociolinguistic theory of naming and titling as markers of status.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Aufsatz aus einem Sammelband
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Benennung; Name; Präsident; Politiker; Frame-Semantik; Social Media
    Lizenz:

    creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess
  14. Detecting the boundaries of sentence-like units on spoken German
    Autor*in: Ruppenhofer, Josef ; Rehbein, Ines
    Erschienen: 2019
    Verlag:  München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg

    Automatic division of spoken language transcripts into sentence-like units is a challenging problem, caused by disfluencies, ungrammatical structures and the lack of punctuation. We present experiments on dividing up German spoken dialogues where we... mehr

     

    Automatic division of spoken language transcripts into sentence-like units is a challenging problem, caused by disfluencies, ungrammatical structures and the lack of punctuation. We present experiments on dividing up German spoken dialogues where we investigate the impact of task setup and data representation, encoding of context information as well as different model architectures for this task.

     
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    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Deutsch; Gesprochene Sprache; Automatische Sprachanalyse; Segmentierung; Satz
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/4.0/deed.de ; info:eu-repo/semantics/openAccess
  15. Overview of GermEval Task 2, 2019 shared task on the identification of offensive language
    Autor*in: Struß, Julia Maria ; Siegel, Melanie ; Ruppenhofer, Josef ; Wiegand, Michael ; Klenner, Manfred
    Erschienen: 2019
    Verlag:  München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg

    We present the second edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. Two subtasks were continued from the first edition, namely a... mehr

     

    We present the second edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. Two subtasks were continued from the first edition, namely a coarse-grained binary classification task and a fine-grained multi-class classification task. As a novel subtask, we introduce the classification of offensive tweets as explicit or implicit. The shared task had 13 participating groups submitting 28 runs for the coarse-grained task, another 28 runs for the fine-grained task, and 17 runs for the implicit-explicit task. We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at projects.fzai.h-da.de/iggsa/

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Deutsch; Beleidigung; Social Media; Twitter <Softwareplattform>; Tweet; Automatische Spracherkennung
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/4.0/deed.de ; info:eu-repo/semantics/openAccess
  16. A descriptive analysis of a German corpus annotated with opinion sources and targets
    Autor*in: Wiegand, Michael ; Lapp, Leonie ; Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg

    We present a descriptive analysis on the two datasets from the shared task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS), the only existing German dataset for opinion role extraction of its size. Our analysis... mehr

     

    We present a descriptive analysis on the two datasets from the shared task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS), the only existing German dataset for opinion role extraction of its size. Our analysis discusses the individual properties of the three components, subjective expressions, sources and targets and their relations towards each other. Our observations should help practitioners and researchers when building a system to extract opinion roles from German data.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Deutsch; Politische Sprache; Gesprochene Sprache; Propositionale Einstellung; Automatische Sprachanalyse
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/4.0/deed.de ; info:eu-repo/semantics/openAccess
  17. A Supervised learning approach for the extraction of opinion sources and targets from German text
    Autor*in: Wiegand, Michael ; Chikobava, Margarita ; Ruppenhofer, Josef
    Erschienen: 2019
    Verlag:  München [u.a.] : German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg

    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... mehr

     

    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.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Deutsch; Semantische Analyse; Propositionale Einstellung; Automatische Sprachanalyse
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/4.0/deed.de ; info:eu-repo/semantics/openAccess