Tagungsbericht "Annotationen in Edition und Forschung. Funktionsbestimmung, Differenzierung und Systematisierung"
Der Terminus „Annotation“ gewinnt mit der fortschreitenden Verankerung der Digital Humanities innerhalb der akademischen Landschaft immer stärker an Bedeutung. Gleichzeitig steht er in den Geistes- und Informationswissenschaften für jeweils...
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Der Terminus „Annotation“ gewinnt mit der fortschreitenden Verankerung der Digital Humanities innerhalb der akademischen Landschaft immer stärker an Bedeutung. Gleichzeitig steht er in den Geistes- und Informationswissenschaften für jeweils unterschiedliche Konzepte, welche zwar in Umfang, Einsatz und Zielausrichtung variieren, aber auch konzeptuelle Parallelen aufweisen. Vor dem Hintergrund der Zusammenarbeit der verschiedenen Wissenschaftsdisziplinen scheint es daher geboten, verschiedene Annotationspraxen und die mit ihnen verbundenen Konzepte von Annotationen zu reflektieren und diskutieren, ins Verhältnis zueinander zu setzen sowie Gemeinsamkeiten und Unterschiedlichkeiten zu systematisieren. Hierfür luden Julia Nantke und Frederik Schlupkothen (beide Bergische Universität Wuppertal) vom Graduiertenkolleg "Dokument – Text – Edition. Bedingungen und Formen ihrer Transformation und Modellierung in transdisziplinärer Perspektive" zur interdisziplinär angelegte Tagung "Annotationen in Edition und Forschung. Funktionsbestimmung, Differenzierung und Systematisierung" vom 20. bis zum 22. Februar 2019 an die Bergische Universität Wuppertal ein. Wissenschaftler*innen aus verschiedenen Ländern und Fachbereichen berichteten in fünf Sektionen über ihre Forschungsprojekte und –ergebnisse zu Annotationen, deren unterschiedlichen Erscheinungsformen und Funktionsweisen sowie zu verschiedenen terminologischen, methodischen und technischen Fragestellungen. Der Annotationsbegriff wurde hierbei bewusst weit gefasst und sowohl auf digitale und analoge sowie manuelle und automatisierte Annotationsprozesse in unterschiedlichen Medien bezogen.
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A Survey on the Role of Negation in Sentiment Analysis
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...
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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.
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A Survey on Hate Speech Detection using Natural Language Processing
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...
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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.
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Convolution Kernels for Opinion Holder Extraction
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...
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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.
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Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction
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...
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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.
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The Role of Knowledge-based Features in Polarity Classification at Sentence Level
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...
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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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A Gold Standard for Relation Extraction in the Food Domain
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...
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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.
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Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
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....
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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.
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Data-driven Knowledge Extraction for the Food Domain
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...
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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.
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