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Argumentation mining in user-generated web discourse Habernal, Ivan; Gurevych, Iryna Zeitschriftenbeitrag | In: Computational Linguistics Journal | 2017 36233 Endnote: Autor*innen: Habernal, Ivan; Gurevych, Iryna
Titel: Argumentation mining in user-generated web discourse
In: Computational Linguistics Journal, 43 (2017) 1, S. 125-179
DOI: 10.1162/COLI_a_00276
URL: http://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00276#.WIDIonpp-nU
Dokumenttyp: 3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Sprache: Englisch
Schlagwörter: Argumentation; Automatisierung; Computerlinguistik; Data Mining; Diskurs; Erziehungswissenschaft; Information Retrieval; Modell; Reliabilität; Soziale Software; Textanalyse; World wide web 2.0
Abstract: The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task. (DIPF/Orig.)
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What is the essence of a claim? Cross-domain claim identification Daxenberger, Johannes; Habernal, Ivan; Stab, Christian; Gurevych, Iryna Sammelbandbeitrag | Aus: Association for Computational Linguistics (Hrsg.): The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017): Proceedings of the conference, September 9-11, 2017, Copenhagen, Denmark | Stroudsburg; PA: Association for Computational Linguistics | 2017 37872 Endnote: Autor*innen: Daxenberger, Johannes; Habernal, Ivan; Stab, Christian; Gurevych, Iryna
Titel: What is the essence of a claim? Cross-domain claim identification
Aus: Association for Computational Linguistics (Hrsg.): The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017): Proceedings of the conference, September 9-11, 2017, Copenhagen, Denmark, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 2045-2056
URL: http://www.aclweb.org/anthology/D/D17/D17-1217.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Argumentation; Computerlinguistik; Data Mining; Qualitative Forschung; Sprachanalyse; Text; Textanalyse
Abstract: Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent conceptualization of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps. (DIPF/Orig.)
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Neural end-to-end learning for computational argumentation mining Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna Sammelbandbeitrag | Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada | Stroudsburg; PA: Association for Computational Linguistics | 2017 37878 Endnote: Autor*innen: Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna
Titel: Neural end-to-end learning for computational argumentation mining
Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 11-22
DOI: 10.18653/v1/P17-1002
URL: https://aclanthology.info/pdf/P/P17/P17-1002.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Argumentation; Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Rhetorik; Semantik; Textanalyse
Abstract: We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiL-STMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning 'natural' subtasks, in a multi-task learning setup, improves performance. (DIPF/Orig.)
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EELECTION at SemEval-2017 Task 10. Ensemble of nEural Learners for kEyphrase ClassificaTION Eger, Steffen; Do Dinh, Erik-Lân; Kuznetsov, Ilia; Kiaeeha, Masoud; 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 37870 Endnote: Autor*innen: Eger, Steffen; Do Dinh, Erik-Lân; Kuznetsov, Ilia; Kiaeeha, Masoud; Gurevych, Iryna
Titel: EELECTION at SemEval-2017 Task 10. Ensemble of nEural Learners for kEyphrase ClassificaTION
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. 942-946
URL: http://aclweb.org/anthology/S17-2163
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Klassifikation; Publikation; Semantik; Textanalyse; Wissenschaft
Abstract: This paper describes our approach to the SemEval 2017 Task 10: "Extracting Keyphrases and Relations from Scientific Publications", specifically to Subtask (B): "Classification of identified keyphrases". We explored three different deep learning approaches: a character-level convolutional neural network (CNN), a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM. From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-F1-score of 0.63 on the test data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four according to this official score. However, we erroneously trained 2 out of 3 neural nets (the stacker and the CNN) on only roughly 15% of the full data, namely, the original development set. When trained on the full data (training+development), our ensemble has a micro-F1-score of 0.69. Our code is available from https://github.com/UKPlab/semeval2017-scienceie. (DIPF/Orig.)
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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 Sammelwerken; 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.)
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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 Sammelwerken; 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.)
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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 Sammelwerken; 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)
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Reporting score distributions makes a difference. Performance study of LSTM-networks for sequence […] Reimers, Nils; Gurevych, Iryna Sammelbandbeitrag | Aus: Association for Computational Linguistics (Hrsg.): The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017): Proceedings of the Conference, September 9-11, 2017, Copenhagen, Denmark | Stroudsburg; PA: Association for Computational Linguistics | 2017 37871 Endnote: Autor*innen: Reimers, Nils; Gurevych, Iryna
Titel: Reporting score distributions makes a difference. Performance study of LSTM-networks for sequence tagging
Aus: Association for Computational Linguistics (Hrsg.): The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017): Proceedings of the Conference, September 9-11, 2017, Copenhagen, Denmark, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 338-348
URL: http://www.aclweb.org/anthology/D/D17/D17-1035.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Spracherkennung
Abstract: In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^-4) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F1-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that produce both superior performance as well as are more stable with respect to the remaining hyperparameters. The full experimental results are published in (Reimers and Gurevych, 2017). The implementation of our network is publicly available. (DIPF/Orig.)
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End-to-end non-factoid question answering with an interactive visualization of neural attention […] Rücklé, Andreas; Gurevych, Iryna Sammelbandbeitrag | Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30 - August 4, 2017: System demonstrations | Stroudsburg; PA: Association for Computational Linguistics | 2017 37876 Endnote: Autor*innen: Rücklé, Andreas; Gurevych, Iryna
Titel: End-to-end non-factoid question answering with an interactive visualization of neural attention weights
Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30 - August 4, 2017: System demonstrations, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 19-24
DOI: 10.18653/v1/P17-4004
URL: https://aclanthology.info/pdf/P/P17/P17-4004.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Aufmerksamkeit; Vernetzung; Modell; Struktur; Analyse; Visualisierung; Forschung; Frage; Antwort; Benutzeroberfläche
Abstract: Advanced attention mechanisms are an important part of sucessful neural network approaches for non-factoid answer selection because they allow the models to focus on few important segments within rather long answer texts. Analyzing attention mechanisms is thus crucial for understanding strengths and weaknesses of particular models. We present an extensible, highly modular service architecture that enables the transformation of neural network models for non-factoid answer selection into fully featured end-to-end question answering systems. The primary objective of our system is to enable researchers a way to interactively explore and compare attention-based neural networks for answer selection. Our interactive user interface helps researchers to better understand the capabilities of the different approaches and can aid qualitative analyses. The source-code of our system is publicly available. (DIPF/Orig.)
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Training argumentation skills with argumentative writing support Stab, Christian; Gurevych, Iryna Sammelbandbeitrag | Aus: Petukhova, Volha; Tian, Ye (Hrsg.): Proceedings of the 21st Workshop on the Semantics and Pragmatics Dialogue (SEMDIAL 2017 SaarDial), Saarbrücken, 15-17 August 2017 | Saarbrücken: Saarland Universität | 2017 37873 Endnote: Autor*innen: Stab, Christian; Gurevych, Iryna
Titel: Training argumentation skills with argumentative writing support
Aus: Petukhova, Volha; Tian, Ye (Hrsg.): Proceedings of the 21st Workshop on the Semantics and Pragmatics Dialogue (SEMDIAL 2017 SaarDial), Saarbrücken, 15-17 August 2017, Saarbrücken: Saarland Universität, 2017 (Proceedings (SemDial)), S. 174-175
URL: www.saardial.uni-saarland.de/wordpress/wp-content/uploads/SemDial2017SaarDial_proceedings.pdf#page=182
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Argumentation; Computerlinguistik; Textanalyse
Abstract: We present an writing support system for assessing written arguments. Our system incorporates three analysis models allowing for rich feedback about argumentation structure, quality of reasons, and presence of opposing arguments. (DIPF/Orig.)
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