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Frame-based data factorizations
Mair, Sebastian; Boubekki, Ahcène; Brefeld, Ulf
Sammelbandbeitrag
| Aus: Precup, Doina; Teh, Yee Whye (Hrsg.): Proceedings of the International Conference on Machine Learning (IMCL 2017), 6-11 August 2017, International Convention Centre, Sydney, Australia | Red Hook; NY: Curran | 2017
37658 Endnote
Autor*innen:
Mair, Sebastian; Boubekki, Ahcène; Brefeld, Ulf
Titel:
Frame-based data factorizations
Aus:
Precup, Doina; Teh, Yee Whye (Hrsg.): Proceedings of the International Conference on Machine Learning (IMCL 2017), 6-11 August 2017, International Convention Centre, Sydney, Australia, Red Hook; NY: Curran, 2017 (Proceedings of Machine Learning Research, 70), S. 2305-2313
URL:
http://proceedings.mlr.press/v70/mair17a/mair17a.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Algorithmus; Automatisierung; Computerlinguistik; Daten; Datenanalyse; Methode; Verfahren
Abstract:
Archetypal Analysis is the method of choice to compute interpretable matrix factorizations. Every data point is represented as a convex combination of factors, i.e., points on the boundary of the convex hull of the data. This renders computation inefficient. In this paper, we show that the set of vertices of a convex hull, the so-called frame, can be efficiently computed by a quadratic program. We provide theoretical and empirical results for our proposed approach and make use of the frame to accelerate Archetypal Analysis. The novel method yields similar reconstruction errors as baseline competitors but is much faster to compute. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
SemEval-2017 task 7. Detection and interpretation of English puns
Miller, Tristan; Hempelmann, Christian; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the workshop, August 3-4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017
37877 Endnote
Autor*innen:
Miller, Tristan; Hempelmann, Christian; Gurevych, Iryna
Titel:
SemEval-2017 task 7. Detection and interpretation of English puns
Aus:
Association for Computational Linguistics (Hrsg.): 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the workshop, August 3-4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 58-68
DOI:
10.18653/v1/S17-2005
URL:
http://aclweb.org/anthology/S17-2005
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Computerlinguistik; Wort; Humor; Rhetorik; Semantik; Linguistik; Phonologie; Automatisierung; Erkennen; Interpretation; System; Evaluation
Abstract:
A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-per-context assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems' methodologies, resources, and results. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
A "Wind of Change" Shaping public opinion of the "Arab Spring" using metaphors
Núñez, Alexandra; Gerloff, Malte; Do Dinh, Erik-Lan; Rapp, Andrea; Gehring, Petra; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Alliance of Digital Humanities (Hrsg.): Digital Humanities 2017: Conference abstracts, McGill University & Université de Montréal, Montréal, Canada, August 8.11, 2017 | Montréal: Alliance of Digital Humanities | 2017
37342 Endnote
Autor*innen:
Núñez, Alexandra; Gerloff, Malte; Do Dinh, Erik-Lan; Rapp, Andrea; Gehring, Petra; Gurevych, Iryna
Titel:
A "Wind of Change" Shaping public opinion of the "Arab Spring" using metaphors
Aus:
Alliance of Digital Humanities (Hrsg.): Digital Humanities 2017: Conference abstracts, McGill University & Université de Montréal, Montréal, Canada, August 8.11, 2017, Montréal: Alliance of Digital Humanities, 2017 , S. 551-553
URL:
https://dh2017.adho.org/abstracts/041/041.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Einflussfaktor; Grammatik; Metapher; Öffentliche Meinung; Presseberichterstattung; Semantik; Textanalyse
Abstract:
How does mass media affect the way we think about controversial topics such as the "Arab Spring"? What persuasive role do metaphors play especially in opinion pieces? We analyze how the political events of the years 2010-2011 in the Middle East and North Africa Region ("Arab Spring") are categorized and assessed using metaphorical constructions in newspaper opinion pieces. We show ways in which particularly the use of metaphors reveals how the media tried to achieve acceptance for the events based on our cultural models (Quinn and Holland, 1987), which are grounded on our western knowledge. To this end, we constructed a pipeline that automatically detects (and filters) metaphors appearing within certain grammatical constructions, before clustering them by presumed source and target domains (Conceptual Metaphor Theory, Lakoff and Johnson, 1980). The results give us insights into how the "Arab Spring" is metaphorically structured by semantic clusters in opinion pieces. (DIPF/Autor)
DIPF-Abteilung:
Informationszentrum Bildung
Automatic processing of text responses in large-scale assessments
Zehner, Fabian
Monographie
| München: mediaTUM | 2016
36670 Endnote
Autor*innen:
Zehner, Fabian
Titel:
Automatic processing of text responses in large-scale assessments
Erscheinungsvermerk:
München: mediaTUM, 2016
URN:
urn:nbn:de:bvb:91-diss-20160803-1296326-1-7
URL:
http://mediatum.ub.tum.de/1077988?show_id=1296326
Dokumenttyp:
1. Monographien (Autorenschaft); Monographie
Sprache:
Englisch
Schlagwörter:
Automatisierung; Codierung; Computer; Geschlechtsspezifischer Unterschied; Mensch; Software; Vergleich; Vergleichsuntersuchung
Abstract:
Beim automatischen Kodieren von Kurztextantworten kategorisiert ein Computer Antworten. In dieser Arbeit wurde eine Software entwickelt, die (i) Textantworten in semantisch homogene Typen gruppiert, (ii) diese Typen kodiert, und (iii) Merkmale extrahiert. Die Ergebnisse zeigen eine annehmbare bis exzellente Übereinstimmung zur menschlichen Kodierung (76-98 %) bei 41.990 Antworten. Zwei weitere Studien zeigen, wie die Software den Erhebungsprozess und inhaltliche Forschung weiterbringen kann. (DIPF/Orig.)
Abstract (english):
{Abstract_englisch}
DIPF-Abteilung:
Bildungsqualität und Evaluation
Generating training data for semantic role labeling based on label transfer from linked lexical […]
Hartmann, Silvana; Eckle-Kohler, Judith; Gurevych, Iryna
Zeitschriftenbeitrag
| In: Transactions of the Association for Computational Linguistics | 2016
36232 Endnote
Autor*innen:
Hartmann, Silvana; Eckle-Kohler, Judith; Gurevych, Iryna
Titel:
Generating training data for semantic role labeling based on label transfer from linked lexical resources
In:
Transactions of the Association for Computational Linguistics, (2016)
URL:
https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/717
Dokumenttyp:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Sprache:
Englisch
Schlagwörter:
Ambiguität; Automatisierung; Computerlinguistik; Computerunterstütztes Verfahren; Semantik; Textanalyse; Wort; Wörterbuch
Abstract (english):
We present a new approach for generating role-labeled training data using Linked Lexical Resources, i.e., integrated lexical resources that combine several resources (e.g., WordNet, FrameNet, Wiktionary) by linking them on the sense or on the role level. Unlike resource-based supervision in relation extraction, we focus on complex linguistic annotations, more specifically FrameNet senses and roles. The automatically labeled training data (http://www.ukp.tu-darmstadt.de/knowledge-based-srl/) are evaluated on four corpora from different domains for the tasks of word sense disambiguation and semantic role classification. Results show that classifiers trained on our generated data equal those resulting from a standard supervised setting. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Automatic coding of short text responses via clustering in educational assessment
Zehner, Fabian; Sälzer, Christine; Goldhammer, Frank
Zeitschriftenbeitrag
| In: Educational and Psychological Measurement | 2016
35473 Endnote
Autor*innen:
Zehner, Fabian; Sälzer, Christine; Goldhammer, Frank
Titel:
Automatic coding of short text responses via clustering in educational assessment
In:
Educational and Psychological Measurement, 76 (2016) 2, S. 280-303
DOI:
10.1177/0013164415590022
URN:
urn:nbn:de:0111-pedocs-149795
URL:
https://nbn-resolving.org/urn:nbn:de:0111-pedocs-149795
Dokumenttyp:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Sprache:
Englisch
Schlagwörter:
Antwort; Automatisierung; Codierung; Computerlinguistik; Leistungstest; Methode; PISA <Programme for International Student Assessment>; Software; Technologiebasiertes Testen; Testkonstruktion
Abstract:
Automatic coding of short text responses opens new doors in assessment. We implemented and integrated baseline methods of natural language processing and statistical modelling by means of software components that are available under open licenses. The accuracy of automatic text coding is demonstrated by using data collected in the Programme for International Student Assessment (PISA) 2012 in Germany. Free text responses of 10 items with Formula responses in total were analyzed. We further examined the effect of different methods, parameter values, and sample sizes on performance of the implemented system. The system reached fair to good up to excellent agreement with human codings Formula Especially items that are solved by naming specific semantic concepts appeared properly coded. The system performed equally well with Formula and somewhat poorer but still acceptable down to Formula Based on our findings, we discuss potential innovations for assessment that are enabled by automatic coding of short text responses. (DIPF/Orig.)
DIPF-Abteilung:
Bildungsqualität und Evaluation
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using […]
Habernal, Ivan; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers | Stroudsburg; PA: Association for Computational Linguistics | 2016
36970 Endnote
Autor*innen:
Habernal, Ivan; Gurevych, Iryna
Titel:
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 1589-1599
URL:
http://www.aclweb.org/anthology/P16-1150
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Algorithmus; Argumentation; Automatisierung; Computerlinguistik; Kommunikation; Online; Prognose; Qualität; Rhetorik; Soziale Software; Textanalyse; Überzeugung; World wide web 2.0
Abstract (english):
We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation "A is more convincing than B" exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman's correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
What makes word-level neural machine translation hard. A case study on English-German translation
Hirschmann, Fabian; Nam, Jinseok; Fürnkranz, Johannes
Sammelbandbeitrag
| Aus: The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING) | Osaka: The COLING 2016 Organizing Committee | 2016
36983 Endnote
Autor*innen:
Hirschmann, Fabian; Nam, Jinseok; Fürnkranz, Johannes
Titel:
What makes word-level neural machine translation hard. A case study on English-German translation
Aus:
The COLING 2016 Organizing Committee (Hrsg.): Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka: The COLING 2016 Organizing Committee, 2016 , S. 3199-3208
URL:
http://aclweb.org/anthology/C/C16/C16-1301.pdf
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Computerunterstütztes Verfahren; Deutsch; Englisch; Syntax; Übersetzung; Wort; Wörterbuch
Abstract (english):
Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT'14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Improve sentiment analysis of citations with author modelling
Ma, Zheng; Nam, Jinseok; Weihe, Karsten
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and Social media analysis (WASSA 2016) held in conjunction with NAACL 2016 | Stroudsburg; PA: Association for Computational Linguistics | 2016
36981 Endnote
Autor*innen:
Ma, Zheng; Nam, Jinseok; Weihe, Karsten
Titel:
Improve sentiment analysis of citations with author modelling
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and Social media analysis (WASSA 2016) held in conjunction with NAACL 2016, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 122-127
URL:
http://www.aclweb.org/anthology/W16-0420
Dokumenttyp:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Autor; Bibliometrie; Modell; Text; Textanalyse; Zitat
Abstract (english):
In this paper, we introduce a novel approach to sentiment polarity classification of citations, which integrates data about the authors' reputation. More specifically, our method extends the h-index with citation polarities and utilizes it in sentiment classification of citation sentences. Our computational results show that our method yields significant improvement in terms of classification performance. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
Token-level metaphor detection using neural networks
Do Dinh, Erik-Lân; Gurevych, Iryna
Sammelbandbeitrag
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the fourth workshop on metaphor in NLP held in conjunction with NAACL 2016 | Stroudsburg; PA: Association for Computational Linguistics | 2016
36978 Endnote
Autor*innen:
Do Dinh, Erik-Lân; Gurevych, Iryna
Titel:
Token-level metaphor detection using neural networks
Aus:
Association for Computational Linguistics (Hrsg.): Proceedings of the fourth workshop on metaphor in NLP held in conjunction with NAACL 2016, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 28-33
URL:
https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/publikationen/2016/2016_DoDinh_NAACL_pages.pdf
Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache:
Englisch
Schlagwörter:
Automatisierung; Computerlinguistik; Netzwerk; Semantik; Textanalyse
Abstract (english):
Automatic metaphor detection usually relies on various features, incorporating e.g. selectional preference violations or concreteness ratings to detect metaphors in text. These features rely on background corpora, hand-coded rules or additional, manually created resources, all specific to the language the system is being used on. We present a novel approach to metaphor detection using a neural network in combination with word embeddings, a method that has already proven to yield promising results for other natural language processing tasks. We show that foregoing manual feature engineering by solely relying on word embeddings trained on large corpora produces comparable results to other systems, while removing the need for additional resources. (DIPF/Orig.)
DIPF-Abteilung:
Informationszentrum Bildung
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