Direkt zur Hauptnavigation springen Direkt zum Inhalt springen
Letzte Suchanfragen

Ergebnisse für *

Es wurden 36 Ergebnisse gefunden.

Zeige Ergebnisse 1 bis 25 von 36.

Sortieren

  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

    Export in Literaturverwaltung
    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. A Survey on the Role of Negation in Sentiment Analysis
    Autor*in: Wiegand, Michael ; Balahur, Alexandra ; Roth, Benjamin ; Klakow, Dietrich ; Montoyo, Andrés
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis. We will present various... mehr

     

    This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects such as level of representation used for sentiment analysis, negation word detection and scope of negation. We will also discuss limits and challenges of negation modeling on that task.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Text Mining; Natürliche Sprache; Negation; Maschinelles Lernen
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  4. A Survey on Hate Speech Detection using Natural Language Processing
    Autor*in: Schmidt, Anna ; Wiegand, Michael
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech... mehr

     

    This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Hassrede; Computerlinguistik; Natürliche Sprache; Text Mining; Social Media
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  5. Convolution Kernels for Opinion Holder Extraction
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate... mehr

     

    Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Information Extraction; Meinung; Natürliche Sprache; Maschinelles Lernen
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  6. Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction being simple unsupervised word clustering, an induction method inspired by distant supervision and the usage of lexical... mehr

     

    In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction being simple unsupervised word clustering, an induction method inspired by distant supervision and the usage of lexical resources. The generalization methods are incorporated into diverse classifiers. We show that generalization causes significant improvements and that the impact of improvement depends on the type of classifier and on how much training and test data differ from each other. We also address the less common case of opinion holders being realized in patient position and suggest approaches including a novel (linguistically-informed) extraction method how to detect those opinion holders without labeled training data as standard datasets contain too few instances of this type.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Information Extraction; Natürliche Sprache; Maschinelles Lernen; Meinung
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  7. The Role of Knowledge-based Features in Polarity Classification at Sentence Level
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Menlo Park, CA : AAAI Press

    Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level... mehr

     

    Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.

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

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  8. Bootstrapping polarity classifiers with rule-based classification
    Autor*in: Wiegand, Michael ; Klenner, Manfred ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Dordrecht : Springer

    In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of... mehr

     

    In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Aufsatz aus einer Zeitschrift
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Polarität; Text Mining; Natürliche Sprache; Maschinelles Lernen
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  9. A Gold Standard for Relation Extraction in the Food Domain
    Autor*in: Wiegand, Michael ; Roth, Benjamin ; Lasarcyk, Eva ; Köser, Stephanie ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Paris : European Language Resources Association

    We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in... mehr

     

    We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Information Extraction; Computerlinguistik; Korpus; Natürliche Sprache; Lebensmittel
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/3.0/ ; info:eu-repo/semantics/openAccess
  10. Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Alicante : Universidad de Alicante

    In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required.... mehr

     

    In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required. Still, this method allows to capture in-domain knowledge by training the supervised classifier on in-domain features, such as bag of words. We investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. The former addresses the issue in how far relevant constructions for polarity classification, such as word sense disambiguation, negation modeling, or intensification, are important for this self-training approach. We not only compare how this method relates to conventional semi-supervised learning but also examine how it performs under more difficult settings in which classes are not balanced and mixed reviews are included in the dataset.

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

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  11. Data-driven Knowledge Extraction for the Food Domain
    Autor*in: Wiegand, Michael ; Roth, Benjamin ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Wien : Österreichische Gesellschaft für Artificial Intelligence

    In this paper, we examine methods to automatically extract domain-specific knowledge from the food domain from unlabeled natural language text. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on... mehr

     

    In this paper, we examine methods to automatically extract domain-specific knowledge from the food domain from unlabeled natural language text. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. We also examine a combination of extraction methods and also consider relationships between different relation types. The extraction methods are applied both on a domain-specific corpus and the domain-independent factual knowledge base Wikipedia. Moreover, we examine an open-domain lexical ontology for suitability.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Information Extraction; Computerlinguistik; Korpus; Empirische Linguistik; Lebensmittel
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  12. The Role of Predicates in Opinion Holder Extraction
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Shoumen : Incoma Ltd.

    In this paper, we investigate the role of predicates in opinion holder extraction. We will examine the shape of these predicates, investigate what relationship they bear towards opinion holders, determine what resources are potentially useful for... mehr

     

    In this paper, we investigate the role of predicates in opinion holder extraction. We will examine the shape of these predicates, investigate what relationship they bear towards opinion holders, determine what resources are potentially useful for acquiring them, and point out limitations of an opinion holder extraction system based on these predicates. For this study, we will carry out an evaluation on a corpus annotated with opinion holders. Our insights are, in particular, important for situations in which no labelled training data are available and only rule-based methods can be applied.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Information Extraction; Computerlinguistik; Prädikat; Maschinelles Lernen; Natürliche Sprache
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/3.0/ ; info:eu-repo/semantics/openAccess
  13. Towards the Detection of Reliable Food-Health Relationships
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    We investigate the task of detecting reliable statements about food-health relationships from natural language texts. For that purpose, we created a specially annotated web corpus from forum entries discussing the healthiness of certain food items.... mehr

     

    We investigate the task of detecting reliable statements about food-health relationships from natural language texts. For that purpose, we created a specially annotated web corpus from forum entries discussing the healthiness of certain food items. We examine a set of task-specific features (mostly) based on linguistic insights that are instrumental in finding utterances that are commonly perceived as reliable. These features are incorporated in a supervised classifier and compared against standard features that are widely used for various tasks in natural language processing, such as bag of words, part-of speech and syntactic parse information.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Natürliche Sprache; Information Extraction; Lebensmittel; Korpus
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess
  14. Prototypical Opinion Holders: What We can Learn from Experts and Analysts
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Shoumen : Incoma Ltd.

    In order to automatically extract opinion holders, we propose to harness the contexts of prototypical opinion holders, i.e. common nouns, such as experts or analysts, that describe particular groups of people whose profession or occupation is to form... mehr

     

    In order to automatically extract opinion holders, we propose to harness the contexts of prototypical opinion holders, i.e. common nouns, such as experts or analysts, that describe particular groups of people whose profession or occupation is to form and express opinions towards specific items. We assess their effectiveness in supervised learning where these contexts are regarded as labelled training data and in rule-based classification which uses predicates that frequently co-occur with mentions of the prototypical opinion holders. Finally, we also examine in how far knowledge gained from these contexts can compensate the lack of large amounts of labeled training data in supervised learning by considering various amounts of actually labeled training sets.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Maschinelles Lernen; Text Mining; Information Extraction
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/3.0/ ; info:eu-repo/semantics/openAccess
  15. Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
    Autor*in: Wiegand, Michael ; Roth, Benjamin ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Stroudsburg, PA : Association for Computational Linguistics

    We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations... mehr

     

    We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Korpus; Text Mining; Maschinelles Lernen; Lebensmittel
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/3.0/ ; info:eu-repo/semantics/openAccess
  16. 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.

     
    Export in Literaturverwaltung
    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
  17. 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.

     
    Export in Literaturverwaltung
    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
  18. Separating Brands from Types: an Investigation of Different Features for the Food Domain
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Dublin : Dublin City University

    We examine the task of separating types from brands in the food domain. Framing the problem as a ranking task, we convert simple textual features extracted from a domain-specific corpus into a ranker without the need of labeled training data. Such... mehr

     

    We examine the task of separating types from brands in the food domain. Framing the problem as a ranking task, we convert simple textual features extracted from a domain-specific corpus into a ranker without the need of labeled training data. Such method should rank brands (e.g. sprite) higher than types (e.g. lemonade). Apart from that, we also exploit knowledge induced by semi-supervised graph-based clustering for two different purposes. On the one hand, we produce an auxiliary categorization of food items according to the Food Guide Pyramid, and assume that a food item is a type when it belongs to a category unlikely to contain brands. On the other hand, we directly model the task of brand detection using seeds provided by the output of the textual ranking features. We also harness Wikipedia articles as an additional knowledge source.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Natürliche Sprache; Information Extraction; Maschinelles Lernen; Lebensmittel
    Lizenz:

    creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
  19. 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.

     
    Export in Literaturverwaltung   RIS-Format
      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
  20. 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/

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    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
  21. 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 ...

     
    Export in Literaturverwaltung   RIS-Format
      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
  22. 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%.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    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
  23. 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.

     
    Export in Literaturverwaltung
    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
  24. Towards Contextual Healthiness Classification of Food Items - A Linguistic Approach
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Nagoya : Asian Federation of Natural Language Processing

    We explore the feasibility of contextual healthiness classification of food items. We present a detailed analysis of the linguistic phenomena that need to be taken into consideration for this task based on a specially annotated corpus extracted from... mehr

     

    We explore the feasibility of contextual healthiness classification of food items. We present a detailed analysis of the linguistic phenomena that need to be taken into consideration for this task based on a specially annotated corpus extracted from web forum entries. For automatic classification, we compare a supervised classifier and rule-based classification. Beyond linguistically motivated features that include sentiment information we also consider the prior healthiness of food items.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Information Extraction; Maschinelles Lernen; Lebensmittel; Natürliche Sprache
    Lizenz:

    creativecommons.org/licenses/by-nc-sa/3.0/ ; info:eu-repo/semantics/openAccess
  25. Convolution Kernels for Subjectivity Detection
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Uppsala : Northern European Association for Language Technology

    In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without... mehr

     

    In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.

     
    Export in Literaturverwaltung
    Quelle: BASE Fachausschnitt Germanistik
    Sprache: Englisch
    Medientyp: Konferenzveröffentlichung
    Format: Online
    DDC Klassifikation: Sprache (400)
    Schlagworte: Computerlinguistik; Natürliche Sprache; Subjektivität; Maschinelles Lernen; Text Mining
    Lizenz:

    rightsstatements.org/page/InC/1.0/ ; info:eu-repo/semantics/openAccess