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  1. Ja und Nein
    der lebendige Gegensatz
    Autor*in: Marx, Bernhard
    Erschienen: [2019]
    Verlag:  Königshausen & Neumann, Würzburg

    Standort:
    Universität Freiburg, Institut für Ethik und Geschichte der Medizin, Bibliothek
    Signatur:
    Frei 38: Phil Allg/567
    Fernleihe:
    keine Ausleihe von Bänden, nur Papierkopien werden versandt
    Link zum Verbundkatalog:
    Südwestdeutscher Bibliotheksverbund (SWB)
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      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Verbundkataloge
    Sprache: Deutsch
    Medientyp: Buch (Monographie)
    Format: Druck
    ISBN: 3826066669; 9783826066665
    Weitere Identifier:
    9783826066665
    Schlagworte: Gegensatz; Polarität; Philosophie;
    Umfang: 134 Seiten, 23 cm
  2. 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.

     
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    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
  3. 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
  4. 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
  5. 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
  6. 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
  7. Predictive Features for Detecting Indefinite Polar Sentences
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Paris : European Language Resources Association

    In recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective and subjective text, i.e. subjectivity detection, and the distinction between positive and negative... mehr

     

    In recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective and subjective text, i.e. subjectivity detection, and the distinction between positive and negative subjective text, i.e. polarity classification. So far, there has been little work examining the distinction between definite polar subjectivity and indefinite polar subjectivity. While the former are utterances which can be categorized as either positive or negative, the latter cannot be categorized as either of these two categories. This paper presents a small set of domain independent features to detect indefinite polar sentences. The features reflect the linguistic structure underlying these types of utterances. We give evidence for the effectiveness of these features by incorporating them into an unsupervised rule-based classifier for sentence-level analysis and compare its performance with supervised machine learning classifiers, i.e. Support Vector Machines (SVMs) and Nearest Neighbor Classifier (kNN). The data used for the experiments are web-reviews collected from three different domains.

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

    creativecommons.org/licenses/by/4.0/deed.de ; info:eu-repo/semantics/openAccess
  8. Detecting conditional healthiness of food items from natural language text
    Autor*in: Wiegand, Michael ; Klakow, Dietrich
    Erschienen: 2019
    Verlag:  Dordrecht : Springer

    In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a... mehr

     

    In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.

     
    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; Information Extraction; Polarität; Lebensmittel; Natürliche Sprache
    Lizenz:

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