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(Schlagwörter: "Klassifikation")

What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in […] Habernal, Ivan; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 2016 conference on Empirical Methods in Natural Language Processing (EMNLP) | Stroudsburg; PA: Association for Computational Linguistics | 2016 36989 Endnote: Author(s): Habernal, Ivan; Gurevych, Iryna
Title: What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 2016 conference on Empirical Methods in Natural Language Processing (EMNLP), Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 1214-1223
URL: https://aclweb.org/anthology/D16-1129
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Argumentation; Bewertung; Computerlinguistik; Klassifikation; Qualität; Überzeugung
Abstract (english): This article tackles a new challenging task in computational argumentation. Given a pair of two arguments to a certain controversial topic, we aim to directly assess qualitative properties of the arguments in order to explain why one argument is more convincing than the other one. We approach this task in a fully empirical manner by annotating 26k explanations written in natural language. These explanations describe convincingness of arguments in the given argument pair, such as their strengths or flaws. We create a new crowd-sourced corpus containing 9,111 argument pairs, multi-labeled with 17 classes, which was cleaned and curated by employing several strict quality measures. We propose two tasks on this data set, namely (1) predicting the full label distribution and (2) classifying types of flaws in less convincing arguments. Our experiments with feature-rich SVM learners and Bidirectional LSTM neural networks with convolution and attention mechanism reveal that such a novel fine-grained analysis of Web argument convincingness is a very challenging task. We release the new UKPConvArg2 corpus and software under permissive licenses to the research community. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Crowdsourcing a large dataset of domain-specific context-sensitive semantic verb relations Sukhareva, Maria; Eckle-Kohler, Judith; Habernal, Ivan; Gurevych, Iryna Book Chapter | Aus: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | Portoroz: European Language Resources Association | 2016 36972 Endnote: Author(s): Sukhareva, Maria; Eckle-Kohler, Judith; Habernal, Ivan; Gurevych, Iryna
Title: Crowdsourcing a large dataset of domain-specific context-sensitive semantic verb relations
In: European Language Resources Association (Hrsg.): Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), Portoroz: European Language Resources Association, 2016 , S. 2131-2137
URL: https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/lrec2016_sukhareva.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Semantik; Textanalyse
Abstract (english): We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Using semantic similarity for multi-label zero-shot classification of text documents Veeranna, Sappadla Prateek; Nam, Jinseok; Mencía, Eneldo Loza; Fürnkranz, Johannes Book Chapter | Aus: European Symposium on Artificial Neural Networks (Hrsg.): ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), 27-29 April 2016 | Bruges: European Symposium on Artificial Neural Networks | 2016 36982 Endnote: Author(s): Veeranna, Sappadla Prateek; Nam, Jinseok; Mencía, Eneldo Loza; Fürnkranz, Johannes
Title: Using semantic similarity for multi-label zero-shot classification of text documents
In: European Symposium on Artificial Neural Networks (Hrsg.): ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), 27-29 April 2016, Bruges: European Symposium on Artificial Neural Networks, 2016 , S. 423-428
URL: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-174.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Computerlinguistik; Klassifikation; Semantik; Text
Abstract (english): In this paper, we examine a simple approach to zero-shot multi-label text classification, i.e., to the problem of predicting multiple, possibly previously unseen labels for a document. In particular, we propose to use a semantic embedding of label and document words and base the prediction of previously unseen labels on the similarity between the label name and the document words in this embedding. Experiments on three textual datasets across various domains show that even such a simple technique yields considerable performance improvements over a simple uninformed baseline. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Social background Watermann, Rainer; Maaz, Kai; Bayer, Sonja; Roczen, Nina Book Chapter | Aus: Kuger, Susanne;Klieme, Eckhard; Jude, Nina; Kaplan, David (Hrsg.): Assessing contexts of learning: An international perspective | Cham: Springer | 2016 36703 Endnote: Author(s): Watermann, Rainer; Maaz, Kai; Bayer, Sonja; Roczen, Nina
Title: Social background
In: Kuger, Susanne;Klieme, Eckhard; Jude, Nina; Kaplan, David (Hrsg.): Assessing contexts of learning: An international perspective, Cham: Springer, 2016 (Methodology of educational measurement and assessment), S. 117-145
DOI: 10.1007/978-3-319-45357-6_5
URL: http://www.springer.com/de/book/9783319453569
Publication Type: 4. Beiträge in Sammelwerken; Sammelband (keine besondere Kategorie)
Language: Englisch
Keywords: Feldstudien; Schüler; Demografische Daten; Finanzen; Kulturelle Aktivität; Soziale Interaktion; Schule; Internationaler Vergleich; Schülerleistung; Leistungsmessung; Sozioökonomische Lage; Einflussfaktor; Theorie; Empirische Forschung; Eltern; Beruf; Klassifikation; Ranking; Bildungsniveau; Einkommen; Sozialkapital; Kulturelles Kapital; PISA <Programme for International Student Assessment>
Abstract (english): Assessing and measuring students' social background characteristics and relating their background data to achievement is pervasive in international large-scale assessments (ILSAs). Our review focuses on two strains of research: the use of socio-economic status (SES) on the one hand, and the use of cultural and social capital on the other. With regard to SES, we provide a brief overview of theoretical concepts, contrasting unidimensional and multidimensional views. We discuss the variety of measures of SES that researchers use in their studies, highlighting the lack of consensus on their conceptual meaning and measurement. We then outline how key indicators of SES (e.g., parental occupation, parental education, parental income) are assessed in ILSAs. This is followed by a section on the quality of students' reports of parent's SES characteristics. With regard to cultural and social capital we discuss the mechanisms that underlie the relationship between social background and students' achievement. In addition, we give a brief overview of research applying the theory of cultural and social capital in the context of ILSAs. Finally, practical implications for the assessment of social background characteristics in ILSA are discussed, and recommendations are offered. Some of these background characteristics were tested in the PISA 2015 field trial. (DIPF/Orig.)
DIPF-Departments: Bildungsqualität und Evaluation; Struktur und Steuerung des Bildungswesens

Multi-view learning with dependent views Brefeld, Ulf Book Chapter | Aus: ACM (Hrsg.): Proceedings of the ACM/SIGAPP Symposium on Applied Computing | New York: Association for Computing Machinery | 2015 35621 Endnote: Author(s): Brefeld, Ulf
Title: Multi-view learning with dependent views
In: ACM (Hrsg.): Proceedings of the ACM/SIGAPP Symposium on Applied Computing, New York: Association for Computing Machinery, 2015 , S. 1-6
URL: https://www.kma.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_KMA/kma_publications/sac2015.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Algorithmus; Computerprogramm; Daten; Klassifikation; Lernen; Text
Abstract: Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Experiments have shown that multi-view learning is sometimes beneficial for problems for which the independence assumption is not satisfied. In practice, unfortunately, it is not possible to measure the dependency between two attribute sets; hence, there is no criterion which allows to decide whether multi-view learning is applicable. We conduct experiments with various text classification problems and investigate on the effectiveness of the co-trained SVM and the co-EM SVM under various conditions, including violations of the independence 0assumption. We identify the error correlation coefficient of the initial classifiers as an elaborate indicator of the expected benefit of multi-view learning. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Linking the thoughts. Analysis of argumentation structures in scientific publications Kirschner, Christian; Eckle-Kohler, Judith; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 2nd Workshop on Argumentation Mining held in conjunction with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT 2015) | Denver; CO: Association for Computational Linguistics | 2015 35503 Endnote: Author(s): Kirschner, Christian; Eckle-Kohler, Judith; Gurevych, Iryna
Title: Linking the thoughts. Analysis of argumentation structures in scientific publications
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 2nd Workshop on Argumentation Mining held in conjunction with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT 2015), Denver; CO: Association for Computational Linguistics, 2015 , S. 1-11
URL: https://aclweb.org/anthology/W/W15/W15-05.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Argumentation; Automatisierung; Bildungsforschung; Computerlinguistik; Data Mining; Klassifikation; Textanalyse; Veröffentlichung
Abstract: This paper presents the results of an annotation study focused on the fine-grained analysis of argumentation structures in scientific publications. Our new annotation scheme specifies four types of binary argumentative relations between sentences, resulting in the representation of arguments as small graph structures. We developed an annotation tool that supports the annotation of such graphs and carried out an annotation study with four annotators on 24 scientific articles from the domain of educational research. For calculating the inter-annotator agreement, we adapted existing measures and developed a novel graph based agreement measure which reflects the semantic similarity of different annotation graphs. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Constructive feedback, thinking process and cooperation. Assessing the quality of classroom […] Sousa, Tahir; Flekova, Lucie; Mieskes, Margot; Gurevych, Iryna Book Chapter | Aus: Möller, Sebastian (Hrsg.): Proceedings of the Interspeech 2015 Conference Dresden | Berlin: Technische Universität | 2015 35635 Endnote: Author(s): Sousa, Tahir; Flekova, Lucie; Mieskes, Margot; Gurevych, Iryna
Title: Constructive feedback, thinking process and cooperation. Assessing the quality of classroom interaction
In: Möller, Sebastian (Hrsg.): Proceedings of the Interspeech 2015 Conference Dresden, Berlin: Technische Universität, 2015 , S. 2739-2743
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Computerlinguistik; Datenanalyse; Denken; Deutschland; Diskursanalyse; Feedback; Interaktionsanalyse; Klassifikation; Kooperation; Mathematikunterricht; Qualität; Schulklasse; Schweiz; Semantik; Soziale Interaktion; Sprachanalyse; Unterrichtsforschung; Video
Abstract: Analyzing and assessing the quality of classroom lessons on a range of quality dimensions is a number one educational research topic, as this allows developing teacher trainings and interventions to improve lesson quality. We model this assessment as a text classification task, exploiting linguistic features to predict the scores in several lesson quality dimensions relevant for educational researchers. Our work relies on a variety of phenomena, amongst them paralinguistic features, such as laughter, from real classroom interactions. We used these features to train machine learning models to assess various quality dimensions of school lessons. Our results show, that especially features focusing on the discourse and semantics are beneficial for this classification task. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

DKPro TC. A Java-based framework for supervised learning experiments on textual data Daxenberger, Johannes; Ferschke, Oliver; Gurevych, Iryna; Zesch, Torsten Book Chapter | Aus: Bontcheva, Kalina; Jingbo, Zhu (Hrsg.): Proceedings of COLING 2014: System demonstrations | Stroudsburg; PA: Association for Computational Linguistics | 2014 34721 Endnote: Author(s): Daxenberger, Johannes; Ferschke, Oliver; Gurevych, Iryna; Zesch, Torsten
Title: DKPro TC. A Java-based framework for supervised learning experiments on textual data
In: Bontcheva, Kalina; Jingbo, Zhu (Hrsg.): Proceedings of COLING 2014: System demonstrations, Stroudsburg; PA: Association for Computational Linguistics, 2014 , S. 61-66
URL: http://aclweb.org/anthology/P/P14/P14-5011.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Automatisierung; Computerlinguistik; Computerprogramm; Data Mining; Datenverarbeitung; Klassifikation; Programmiersprache; Text; Textanalyse
Abstract: We present DKPro TC, a framework for supervised learning experiments on textual data. The main goal of DKPro TC is to enable researchers to focus on the actual research task behind the learning problem and let the framework handle the rest. It enables rapid prototyping of experiments by relying on an easy-to-use workflow engine and standardized document preprocessing based on the Apache Unstructured Information Management Architecture (Ferrucci and Lally, 2004). It ships with standard feature extraction modules, while at the same time allowing the user to add customized extractors. The extensive reporting and logging facilities make DKPro TC experiments fully replicable. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

UKPDIPF. A lexical semantic approach to sentiment polarity prediction in Twitter data Flekova, Lucie; Ferschke, Oliver; Gurevych, Iryna Book Chapter | Aus: Nakov, Preslav; Zesch, Torsten (Hrsg.): Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) | Stroudsburg; PA: Association for Computational Linguistics | 2014 34724 Endnote: Author(s): Flekova, Lucie; Ferschke, Oliver; Gurevych, Iryna
Title: UKPDIPF. A lexical semantic approach to sentiment polarity prediction in Twitter data
In: Nakov, Preslav; Zesch, Torsten (Hrsg.): Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Stroudsburg; PA: Association for Computational Linguistics, 2014 , S. 704-710
URL: http://alt.qcri.org/semeval2014/cdrom/pdf/SemEval2014126.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Ausdruck <Psy>; Computerlinguistik; Emotion; Klassifikation; Schriftsprache; Semantik; Soziale Software; Textanalyse
Abstract: We present a sentiment classification system that participated in the SemEval 2014 shared task on sentiment analysis in Twitter. Our system expands tokens in a tweet with semantically similar expressions using a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word lists repre- senting positive and negative sentiment. This approach helps to assess the polarity of tweets that do not directly contain polarity cues. Moreover, we incorporate syntactic, lexical and surface sentiment features. On the message level, our system achieved the 8th place in terms of macroaveraged F-score among 50 systems, with particularly good performance on the Life-Journal corpus (F1=71.92) and the Twitter sarcasm (F1=54.59) dataset. On the expression level, our system ranked 14 out of 27 systems, based on macro-averaged F-score. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung

Annotating argument components and relations in persuasive essays Stab, Christian; Gurevych, Iryna Book Chapter | Aus: Tsujii, Junichi;Hajic, Jan (Hrsg.): Proceedings of COLING 2014: Technical papers | Stroudsburg; PA: Association for Computational Linguistics | 2014 34976 Endnote: Author(s): Stab, Christian; Gurevych, Iryna
Title: Annotating argument components and relations in persuasive essays
In: Tsujii, Junichi;Hajic, Jan (Hrsg.): Proceedings of COLING 2014: Technical papers, Stroudsburg; PA: Association for Computational Linguistics, 2014 , S. 1501-1510
URL: http://anthology.aclweb.org/C/C14/C14-1142.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Argumentation; Automatisierung; Computerlinguistik; Diskurs; Klassifikation; Modell; Qualität; Reliabilität; Text; Textanalyse
Abstract: In this paper, we present a novel approach to model arguments, their components and relations in persuasive essays in English. We propose an annotation scheme that includes the annotation of claims and premises as well as support and attack relations for capturing the structure of argumentative discourse. We further conduct a manual annotation study with three annotators on 90 persuasive essays. The obtained inter-rater agreement of αU = 0.72 for argument components and α = 0.81 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to substantial agreement. The final corpus and the annotation guidelines are freely available to encourage future research in argument recognition. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung