Computational Social Science - Publications (new) (Universität Paderborn)

Identifying Feedback Types to Augment Feedback Comment Generation

M. Stahl, H. Wachsmuth, in: Proceedings of the 16th International Natural Language Generation Conference, 2023

In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitask-learning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing feedback generation only.

@inproceedings{Stahl_Wachsmuth, title={Identifying Feedback Types to Augment Feedback Comment Generation}, booktitle={Proceedings of the 16th International Natural Language Generation Conference}, author={Stahl, Maja and Wachsmuth, Henning} }

"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain

H. Wachsmuth, M. Alshomary, in: Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 344 - 354

Citation (Bibtex)

@inproceedings{Wachsmuth_Alshomary_2022, title={“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain}, booktitle={Proceedings of the 29th International Conference on Computational Linguistics}, author={Wachsmuth, Henning and Alshomary, Milad}, year={2022}, pages={344–354} }

Analyzing Culture-Specific Argument Structures in Learner Essays

W. Chen, M. Chen, G. Mudgal, H. Wachsmuth, in: Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51 - 61

Citation (Bibtex)

@inproceedings{Chen_Chen_Mudgal_Wachsmuth_2022, title={Analyzing Culture-Specific Argument Structures in Learner Essays}, booktitle={Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)}, author={Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning}, year={2022}, pages={51–61} }

Argument Novelty and Validity Assessment via Multitask and Transfer Learning

M. Alshomary, M. Stahl, in: Proceedings of the 9th Workshop on Argument Mining, International Conference on Computational Linguistics, 2022, pp. 111–114

Abstract

Citation (Bibtex)

An argument is a constellation of premises reasoning towards a certain conclusion. The automatic generation of conclusions is becoming a very prominent task, raising the need for automatic measures to assess the quality of these generated conclusions. The SharedTask at the 9th Workshop on Argument Mining proposes a new task to assess the novelty and validity of a conclusion given a set of premises. In this paper, we present a multitask learning approach that transfers the knowledge learned from the natural language inference task to the tasks at hand. Evaluation results indicate the importance of both knowledge transfer and joint learning, placing our approach in the fifth place with strong results compared to baselines.

@inproceedings{Alshomary_Stahl_2022, place={Online and in Gyeongju, Republic of Korea}, title={Argument Novelty and Validity Assessment via Multitask and Transfer Learning}, booktitle={Proceedings of the 9th Workshop on Argument Mining}, publisher={International Conference on Computational Linguistics}, author={Alshomary, Milad and Stahl, Maja}, year={2022}, pages={111–114} }

Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning

M. Sengupta, M. Alshomary, H. Wachsmuth, in: Proceedings of the 2022 Workshop on Figurative Language Processing, 2022

Citation (Bibtex)

@inproceedings{Sengupta_Alshomary_Wachsmuth_2022, title={Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning}, booktitle={Proceedings of the 2022 Workshop on Figurative Language Processing}, author={Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning}, year={2022} }

Generating Contrastive Snippets for Argument Search

M. Alshomary, J. Rieskamp, H. Wachsmuth, in: Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21 - 31

Citation (Bibtex)

DOI

@inproceedings{Alshomary_Rieskamp_Wachsmuth_2022, title={Generating Contrastive Snippets for Argument Search}, DOI={http://dx.doi.org/10.3233/FAIA220138}, booktitle={Proceedings of the 9th International Conference on Computational Models of Argument}, author={Alshomary, Milad and Rieskamp, Jonas and Wachsmuth, Henning}, year={2022}, pages={21–31} }

Identifying the Human Values behind Arguments

J. Kiesel, M. Alshomary, N. Handke, X. Cai, H. Wachsmuth, B. Stein, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459 - 4471

Citation (Bibtex)

@inproceedings{Kiesel_Alshomary_Handke_Cai_Wachsmuth_Stein_2022, title={Identifying the Human Values behind Arguments}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, author={Kiesel, Johannes and Alshomary, Milad and Handke, Nicolas and Cai, Xiaoni and Wachsmuth, Henning and Stein, Benno}, year={2022}, pages={4459–4471} }

Investigating the argumentation structures of EFL learners from diverse language backgrounds

M. Chen, G. Mudgal, W. Chen, H. Wachsmuth. Investigating the argumentation structures of EFL learners from diverse language backgrounds. In: , 2022.

Citation (Bibtex)

@inproceedings{Chen_Mudgal_Chen_Wachsmuth_2022, title={Investigating the argumentation structures of EFL learners from diverse language backgrounds}, booktitle={EUROCALL}, author={Chen, Mei-Hua and Mudgal, Garima and Chen, Wei-Fan and Wachsmuth, Henning}, year={2022} }

Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing

M. Ahmed, Master's thesis, 2022

Abstract

Citation (Bibtex)

This thesis aims to provide a bidirectional chatbot solution for the requirement engineering process. The Sonderforschungsbereich (SFB) 901 intends to provide the composition of software service On-the-Fly (OTF). The sub-project (B1) of the SFB 901 project deals with the parameters of service configuration. OTF Computing aims to eradicate the dependency on the requirement engineers for the software development process. However, there is no existing bidirectional chatbot solution that analyses user software requirements and provides viable suggestions to the user regarding their service. Previously, CORDULA chatbot was developed to analyze the software requirements but cannot keep the conversation’s context. The Rasa framework is integrated with the knowledge base to solve the issue, the knowledge base provides domain-specific knowledge to the chatbot. The software description is passed through the natural language understanding process to give consciousness to the chatbot. This process involves various machine learning models, including app family classification, to correctly identify the domain for user OTF service. The statistical models like naïve Bayes, kNN and SVM are compared with transformer models for this classification task. Furthermore, the entities (functional requirements) are also separated from the user description. The chatbot provides the suggestion of requirements from the preliminary service template with the support of the knowledge base. Furthermore, the generated response is compared with the state-of-the-art DialoGPT transformer model and ChatterBot conversational library. These models are trained over the software development related conversational dataset. All the responses are ranked using the DialoRPT model, and the BLEU score to evaluates the models’ responses. Moreover, the chatbot mod- els are tested with human participants, they used and scored the chatbot responses based on effectiveness, efficiency and satisfaction. The overall response accuracy is also measured by averaging the user approval over the generated responses.

@book{Ahmed_2022, title={Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing}, author={Ahmed, Mobeen}, year={2022} }

No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media

M. Spliethöver, M. Keiff, H. Wachsmuth, in: Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Association for Computational Linguistics, 2022

Abstract

Citation (Bibtex)

arXiv

News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.

@inproceedings{Spliethöver_Keiff_Wachsmuth_2022, title={No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media}, booktitle={Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)}, publisher={Association for Computational Linguistics}, author={Spliethöver, Maximilian and Keiff, Maximilian and Wachsmuth, Henning}, year={2022} }

On the Role of Knowledge in Computational Argumentation

A. Lauscher, H. Wachsmuth, I. Gurevych, G. Glavaš, Transactions of the Association for Computational Linguistics (2022)

Citation (Bibtex)

@article{Lauscher_Wachsmuth_Gurevych_Glavaš_2022, title={On the Role of Knowledge in Computational Argumentation}, journal={Transactions of the Association for Computational Linguistics}, author={Lauscher, Anne and Wachsmuth, Henning and Gurevych, Iryna and Glavaš, Goran}, year={2022} }

Overview of Touché 2022: Argument Retrieval

A. Bondarenko, M. Fröbe, J. Kiesel, S. Syed, T. Gurcke, M. Beloucif, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: Lecture Notes in Computer Science, Springer International Publishing, 2022

Citation (Bibtex)

DOI

@inbook{Bondarenko_Fröbe_Kiesel_Syed_Gurcke_Beloucif_Panchenko_Biemann_Stein_Wachsmuth_et al._2022, place={Cham}, title={Overview of Touché 2022: Argument Retrieval}, DOI={10.1007/978-3-030-99739-7_43}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Bondarenko, Alexander and Fröbe, Maik and Kiesel, Johannes and Syed, Shahbaz and Gurcke, Timon and Beloucif, Meriem and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and et al.}, year={2022} }

The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments

M. Alshomary, R. El Baff, T. Gurcke, H. Wachsmuth, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 8782 - 8797

Citation (Bibtex)

@inproceedings{Alshomary_El Baff_Gurcke_Wachsmuth_2022, title={The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, author={Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning}, year={2022}, pages={8782–8797} }

To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation

M. Stahl, M. Spliethöver, H. Wachsmuth, in: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science, 2022

Abstract

Citation (Bibtex)

Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better.

@inproceedings{Stahl_Spliethöver_Wachsmuth, place={Abu Dhabi, United Arab Emirates}, title={To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation}, booktitle={Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science}, author={Stahl, Maja and Spliethöver, Maximilian and Wachsmuth, Henning} }

Argument Undermining: Counter-Argument Generation by Attacking Weak Premises

M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), Association for Computational Linguistics, 2021, pp. 1816–1827

Citation (Bibtex)

DOI

@inproceedings{Alshomary_Syed_Potthast_Wachsmuth_2021, series={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021}, title={Argument Undermining: Counter-Argument Generation by Attacking Weak Premises}, DOI={10.18653/v1/2021.findings-acl.159}, booktitle={Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}, publisher={Association for Computational Linguistics}, author={Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}, year={2021}, pages={1816–1827}, collection={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021} }

Assessing the Sufficiency of Arguments through Conclusion Generation

T. Gurcke, M. Alshomary, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 67 - 77

Citation (Bibtex)

@inproceedings{Gurcke_Alshomary_Wachsmuth_2021, title={Assessing the Sufficiency of Arguments through Conclusion Generation}, booktitle={Proceedings of the 8th Workshop on Argument Mining}, author={Gurcke, Timon and Alshomary, Milad and Wachsmuth, Henning}, year={2021}, pages={67–77} }

Belief-based Generation of Argumentative Claims

M. Alshomary, W. Chen, T. Gurcke, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, 2021, pp. 224-223

Abstract

Citation (Bibtex)

When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

@inproceedings{Alshomary_Chen_Gurcke_Wachsmuth_2021, title={Belief-based Generation of Argumentative Claims}, booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}, publisher={Association for Computational Linguistics}, author={Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning}, year={2021}, pages={224–223} }

Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models

M. Spliethöver, H. Wachsmuth, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 552-559

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Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.

@inproceedings{Spliethöver_Wachsmuth_2021, title={Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models}, DOI={10.24963/ijcai.2021/77}, booktitle={Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21}, author={Spliethöver, Maximilian and Wachsmuth, Henning}, year={2021}, pages={552–559} }

https://www.ijcai.org/proceedings/2021/77

Controlled Neural Sentence-Level Reframing of News Articles

W. Chen, K. Al Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683 - 2693

Citation (Bibtex)

@inproceedings{Chen_Al Khatib_Stein_Wachsmuth_2021, title={Controlled Neural Sentence-Level Reframing of News Articles}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}, author={Chen, Wei-Fan and Al Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}, year={2021}, pages={2683–2693} }

Employing Argumentation Knowledge Graphs for Neural Argument Generation

K. Al-Khatib, L. Trautner, H. Wachsmuth, Y. Hou, B. Stein, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021, pp. 4744-4754

Citation (Bibtex)

@inproceedings{Al-Khatib_Trautner_Wachsmuth_Hou_Stein_2021, title={Employing Argumentation Knowledge Graphs for Neural Argument Generation}, booktitle={Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}, author={Al-Khatib, Khalid and Trautner, Lukas and Wachsmuth, Henning and Hou, Yufang and Stein, Benno}, year={2021}, pages={4744–4754} }

Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems

K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier, E. Esposito, A. Grimminger, B. Hammer, R. Häb-Umbach, I. Horwath, E. Hüllermeier, F. Kern, S. Kopp, K. Thommes, A. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P. Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems (2021), 13(3), pp. 717-728

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DOI

@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Häb-Umbach_et al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}, volume={13}, DOI={10.1109/tcds.2020.3044366}, number={3}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Häb-Umbach, Reinhold and et al.}, year={2021}, pages={717–728} }

Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of Al Systems

K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier, E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Huellermeier, F. Kern, S. Kopp, K. Thommes, A. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P. Wagner, B. Wrede, IEEE Transactions on Cognitive and Development Systems (2021), 13(3), pp. 717-728

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The recent surge of interest in explainability in artificial intelligence (XAI) is propelled by not only technological advancements in machine learning but also by regulatory initiatives to foster transparency in algorithmic decision making. In this article, we revise the current concept of explainability and identify three limitations: passive explainee, narrow view on the social process, and undifferentiated assessment of explainee’s understanding. In order to overcome these limitations, we present explanation as a social practice in which explainer and explainee co-construct understanding on the microlevel. We view the co-construction on a microlevel as embedded into a macrolevel, yielding expectations concerning, e.g., social roles or partner models: typically, the role of the explainer is to provide an explanation and to adapt it to the current level of explainee’s understanding; the explainee, in turn, is expected to provide cues that direct the explainer. Building on explanations being a social practice, we present a conceptual framework that aims to guide future research in XAI. The framework relies on the key concepts of monitoring and scaffolding to capture the development of interaction. We relate our conceptual framework and our new perspective on explaining to transparency and autonomy as objectives considered for XAI.

@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Haeb-Umbach_et al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of Al Systems}, volume={13}, DOI={10.1109/TCDS.2020.3044366}, number={3}, journal={IEEE Transactions on Cognitive and Development Systems}, author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and et al.}, year={2021}, pages={717–728} }

article_09_2021_haebumbach.pdf -

Generating Informative Conclusions for Argumentative Texts

S. Syed, K. Al-Khatib, M. Alshomary, H. Wachsmuth, M. Potthast, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021): Findings, 2021, pp. 3482-3493

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@inproceedings{Syed_Al-Khatib_Alshomary_Wachsmuth_Potthast_2021, title={Generating Informative Conclusions for Argumentative Texts}, booktitle={Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021): Findings}, author={Syed, Shahbaz and Al-Khatib, Khalid and Alshomary, Milad and Wachsmuth, Henning and Potthast, Martin}, year={2021}, pages={3482–3493} }

https://aclanthology.org/2021.findings-acl.306.pdf

iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing

Z. Nouri, N. Prakash, U. Gadiraju, H. Wachsmuth, in: Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021, 2021

Citation (Bibtex)

@inproceedings{Nouri_Prakash_Gadiraju_Wachsmuth_2021, title={iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing}, booktitle={Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021}, author={Nouri, Zahra and Prakash, Nikhil and Gadiraju, Ujwal and Wachsmuth, Henning}, year={2021} }

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184 - 189

Citation (Bibtex)

@inproceedings{Alshomary_Gurcke_Syed_Heinisch_Spliethöver_Cimiano_Potthast_Wachsmuth_2021, title={Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}, booktitle={Proceedings of the 8th Workshop on Argument Mining}, author={Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}, year={2021}, pages={184–189} }

Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale

G. Skitalinskaya, J. Klaff, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718-1729

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@inproceedings{Skitalinskaya_Klaff_Wachsmuth_2021, title={Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}, booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}, author={Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}, year={2021}, pages={1718–1729} }

Overview of Touché 2021: Argument Retrieval

A. Bondarenko, L. Gienapp, M. Fröbe, M. Beloucif, Y. Ajjour, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: Proceedings of the 43rd annual European Conference on Information Retrieval Research, 2021, pp. 384-395

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@inproceedings{Bondarenko_Gienapp_Fröbe_Beloucif_Ajjour_Panchenko_Biemann_Stein_Wachsmuth_Potthast_et al._2021, title={Overview of Touché 2021: Argument Retrieval}, booktitle={Proceedings of the 43rd annual European Conference on Information Retrieval Research}, author={Bondarenko, Alexander and Gienapp, Lukas and Fröbe, Maik and Beloucif, Meriem and Ajjour, Yamen and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and et al.}, year={2021}, pages={384–395} }

Syntopical Graphs for Computational Argumentation Tasks

J. Barrow, R. Jain, N. Lipka, F. Dernoncourt, V. Morariu, V. Manjunatha, D. Oard, P. Resnik, H. Wachsmuth, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021, pp. 1583-1595

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@inproceedings{Barrow_Jain_Lipka_Dernoncourt_Morariu_Manjunatha_Oard_Resnik_Wachsmuth_2021, title={Syntopical Graphs for Computational Argumentation Tasks}, booktitle={Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}, author={Barrow, Joe and Jain, Rajiv and Lipka, Nedim and Dernoncourt, Franck and Morariu, Vlad and Manjunatha, Varun and Oard, Douglas and Resnik, Philip and Wachsmuth, Henning}, year={2021}, pages={1583–1595} }

https://aclanthology.org/2021.acl-long.126.pdf

The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases

J. Kiesel, D. Spina, H. Wachsmuth, B. Stein, in: Proceedings of the 2021 Conversational User Interfaces Conference, 2021, pp. 1-5

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@inproceedings{Kiesel_Spina_Wachsmuth_Stein_2021, title={The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases}, booktitle={Proceedings of the 2021 Conversational User Interfaces Conference}, author={Kiesel, Johannes and Spina, Damiano and Wachsmuth, Henning and Stein, Benno}, year={2021}, pages={1–5} }

Abstractive Snippet Generation

W. Chen, S. Syed, B. Stein, M. Hagen, M. Potthast, in: Proceedings of the Web Conference 2020, 2020, pp. 1309-1319

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@inproceedings{Chen_Syed_Stein_Hagen_Potthast_2020, title={Abstractive Snippet Generation}, booktitle={Proceedings of the Web Conference 2020}, author={Chen, Wei-Fan and Syed, Shahbaz and Stein, Benno and Hagen, Matthias and Potthast, Martin}, year={2020}, pages={1309–1319} }

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

W. Chen, K. Al-Khatib, H. Wachsmuth, B. Stein, in: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020, pp. 149-154

Citation (Bibtex)

@inproceedings{Chen_Al-Khatib_Wachsmuth_Stein_2020, title={Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity}, booktitle={Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}, author={Chen, Wei-Fan and Al-Khatib, Khalid and Wachsmuth, Henning and Stein, Benno}, year={2020}, pages={149–154} }

Analyzing the Persuasive Effect of Style in News Editorial Argumentation

R. El Baff, H. Wachsmuth, K. Al-Khatib, B. Stein, in: Proceedings of 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 553-564

Citation (Bibtex)

@inproceedings{El Baff_Wachsmuth_Al-Khatib_Stein_2020, title={Analyzing the Persuasive Effect of Style in News Editorial Argumentation}, booktitle={Proceedings of 58th Annual Meeting of the Association for Computational Linguistics}, author={El Baff, Roxanne and Wachsmuth, Henning and Al-Khatib, Khalid and Stein, Benno}, editor={Tsujii, Junichi and Hajic, JanEditors}, year={2020}, pages={553–564} }

Argument from Old Man's View: Assessing Social Bias in Argumentation

M. Spliethöver, H. Wachsmuth, in: Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 76-87

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@inproceedings{Spliethöver_Wachsmuth_2020, title={Argument from Old Man’s View: Assessing Social Bias in Argumentation}, booktitle={Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)}, author={Spliethöver, Maximilian and Wachsmuth, Henning}, year={2020}, pages={76–87} }

https://www.aclweb.org/anthology/2020.argmining-1.9

CauseNet: Towards a Causality Graph Extracted from the Web

S. Heindorf, Y. Scholten, H. Wachsmuth, A. Ngonga Ngomo, M. Potthast, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, pp. 3023-3030

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@inproceedings{Heindorf_Scholten_Wachsmuth_Ngonga Ngomo_Potthast_2020, title={CauseNet: Towards a Causality Graph Extracted from the Web}, DOI={10.1145/3340531.3412763}, booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020)}, author={Heindorf, Stefan and Scholten, Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin}, year={2020}, pages={3023–3030} }

https://papers.dice-research.org/2020/CIKM-20/heindorf_2020a_public.pdf

Detecting Media Bias in News Articles using Gaussian Bias Distributions

W. Chen, K. Al-Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 4290-4300

Citation (Bibtex)

@inproceedings{Chen_Al-Khatib_Stein_Wachsmuth_2020, title={Detecting Media Bias in News Articles using Gaussian Bias Distributions}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2020}, author={Chen, Wei-Fan and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}, year={2020}, pages={4290–4300} }

End-to-End Argumentation Knowledge Graph Construction

K. Al-Khatib, Y. Hou, H. Wachsmuth, C. Jochim, F. Bonin, B. Stein, in: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), 2020, pp. 7367 - 7374

Citation (Bibtex)

@inproceedings{Al-Khatib_Hou_Wachsmuth_Jochim_Bonin_Stein_2020, title={End-to-End Argumentation Knowledge Graph Construction}, booktitle={Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)}, author={Al-Khatib, Khalid and Hou, Yufang and Wachsmuth, Henning and Jochim, Charles and Bonin, Francesca and Stein, Benno}, year={2020}, pages={7367–7374} }

Extractive Snippet Generation for Arguments

M. Alshomary, N. Düsterhus, H. Wachsmuth, in: Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1969-1972

Citation (Bibtex)

@inproceedings{Alshomary_Düsterhus_Wachsmuth_2020, title={Extractive Snippet Generation for Arguments}, booktitle={Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, author={Alshomary, Milad and Düsterhus, Nick and Wachsmuth, Henning}, year={2020}, pages={1969–1972} }

Intrinsic Quality Assessment of Arguments

H. Wachsmuth, T. Werner, in: Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, 2020, pp. 6739-6745

Citation (Bibtex)

@inproceedings{Wachsmuth_Werner_2020, title={Intrinsic Quality Assessment of Arguments}, booktitle={Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics}, author={Wachsmuth, Henning and Werner, Till}, year={2020}, pages={6739–6745} }

Investigating Expectations for Voice-based and Conversational Argument Search on the Web

J. Kiesel, K. Lang, H. Wachsmuth, E. Hornecker, B. Stein, in: Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2020), 2020, pp. 53-62

Citation (Bibtex)

@inproceedings{Kiesel_Lang_Wachsmuth_Hornecker_Stein_2020, title={Investigating Expectations for Voice-based and Conversational Argument Search on the Web}, booktitle={Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2020)}, author={Kiesel, Johannes and Lang, Kevin and Wachsmuth, Henning and Hornecker, Eva and Stein, Benno}, year={2020}, pages={53–62} }

Mining Crowdsourcing Problems from Discussion Forums of Workers

Z. Nouri, H. Wachsmuth, G. Engels, in: Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, 2020, pp. 6264-6276

Citation (Bibtex)

@inproceedings{Nouri_Wachsmuth_Engels, title={Mining Crowdsourcing Problems from Discussion Forums of Workers}, booktitle={Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics}, author={Nouri, Zahra and Wachsmuth, Henning and Engels, Gregor}, pages={6264–6276} }

Overview of Touché 2020: Argument Retrieval

A. Bondarenko, M. Fröbe, M. Beloucif, L. Gienapp, Y. Ajjour, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: CEUR Workshop Proceedings, 2020, pp. 384-395

Citation (Bibtex)

@inproceedings{Bondarenko_Fröbe_Beloucif_Gienapp_Ajjour_Panchenko_Biemann_Stein_Wachsmuth_Potthast_et al._2020, title={Overview of Touché 2020: Argument Retrieval}, volume={2696}, booktitle={CEUR Workshop Proceedings}, author={Bondarenko, Alexander and Fröbe, Maik and Beloucif, Meriem and Gienapp, Lukas and Ajjour, Yamen and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and et al.}, year={2020}, pages={384–395} }

Persuasiveness of News Editorials depending on Ideology and Personality

R. El Baff, K. Al-Khatib, B. Stein, H. Wachsmuth, in: Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES 2020), 2020, pp. 29-40

Citation (Bibtex)

@inproceedings{El Baff_Al-Khatib_Stein_Wachsmuth_2020, title={Persuasiveness of News Editorials depending on Ideology and Personality}, booktitle={Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES 2020)}, author={El Baff, Roxanne and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}, year={2020}, pages={29–40} }

Playful User-Generated Treatment: A Novel Game Design Approach for VR Exposure Therapy

D. Alexandrovsky, G. Volkmar, M. Spliethöver, S. Finke, M. Herrlich, T. Döring, J.D. Smeddinck, R. Malaka, in: Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Association for Computing Machinery, 2020, pp. 32–45

Abstract

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Overcoming a range of challenges that traditional therapy faces, VRET yields great potential for the treatment of phobias such as acrophobia, the fear of heights. We investigate this potential and present playful user-generated treatment (PUT), a novel game-based approach for VRET. Based on a requirement analysis consisting of a literature review and semi-structured interviews with professional therapists, we designed and implemented the PUT concept as a two-step VR game design. To validate our approach, we conducted two studies. (1) In a study with 31 non-acrophobic subjects, we investigated the effect of content creation on player experience, motivation and height perception, and (2) in an online survey, we collected feedback from professional therapists. Both studies reveal that the PUT approach is well applicable. In particular, the analysis of the user study shows that the design phase leads to increased interest and enjoyment without notably influencing affective measures during the exposure session. Our work can help guiding researchers and practitioners at the intersection of game design and exposure therapy.

@inproceedings{Alexandrovsky_Volkmar_Spliethöver_Finke_Herrlich_Döring_Smeddinck_Malaka_2020, place={New York, NY, USA}, series={CHI PLAY’20}, title={Playful User-Generated Treatment: A Novel Game Design Approach for VR Exposure Therapy}, DOI={10.1145/3410404.3414222}, booktitle={Proceedings of the Annual Symposium on Computer-Human Interaction in Play}, publisher={Association for Computing Machinery}, author={Alexandrovsky, Dmitry and Volkmar, Georg and Spliethöver, Maximilian and Finke, Stefan and Herrlich, Marc and Döring, Tanja and Smeddinck, Jan David and Malaka, Rainer}, year={2020}, pages={32–45}, collection={CHI PLAY’20} }

Semi-Supervised Cleansing of Web Argument Corpora

J. Dorsch, H. Wachsmuth, in: Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 19-29

Citation (Bibtex)

@inproceedings{Dorsch_Wachsmuth_2020, title={Semi-Supervised Cleansing of Web Argument Corpora}, booktitle={Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)}, author={Dorsch, Jonas and Wachsmuth, Henning}, year={2020}, pages={19–29} }

Target Inference in Argument Conclusion Generation

M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Association for Computational Linguistics, 2020, pp. 4334-4345

Citation (Bibtex)

@inproceedings{Alshomary_Syed_Potthast_Wachsmuth_2020, series={Proceedings of 58th Annual Meeting of the Association for Computational Linguistics}, title={Target Inference in Argument Conclusion Generation}, booktitle={Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}, publisher={Association for Computational Linguistics}, author={Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}, year={2020}, pages={4334–4345}, collection={Proceedings of 58th Annual Meeting of the Association for Computational Linguistics} }

Task Proposal: Abstractive Snippet Generation for Web Pages

S. Syed, W. Chen, M. Hagen, B. Stein, H. Wachsmuth, M. Potthast, in: Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020), 2020, pp. 237-241

Citation (Bibtex)

@inproceedings{Syed_Chen_Hagen_Stein_Wachsmuth_Potthast_2020, title={Task Proposal: Abstractive Snippet Generation for Web Pages}, booktitle={Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020)}, author={Syed, Shahbaz and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Wachsmuth, Henning and Potthast, Martin}, year={2020}, pages={237–241} }

Argument Search: Assessing Argument Relevance

M. Potthast, L. Gienapp, F. Euchner, N. Heilenkötter, N. Weidmann, H. Wachsmuth, B. Stein, M. Hagen, in: 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019), ACM, 2019, pp. 1117 - 1120

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@inproceedings{Potthast_Gienapp_Euchner_Heilenkötter_Weidmann_Wachsmuth_Stein_Hagen_2019, title={Argument Search: Assessing Argument Relevance}, DOI={10.1145/3331184.3331327}, booktitle={42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019)}, publisher={ACM}, author={Potthast, Martin and Gienapp, Lukas and Euchner, Florian and Heilenkötter, Nick and Weidmann, Nico and Wachsmuth, Henning and Stein, Benno and Hagen, Matthias}, year={2019}, pages={1117–1120} }

Book Review: Argumentation Mining

H. Wachsmuth, Computational Linguistics, ACL (2019), pp. 603 - 606

Citation (Bibtex)

@article{Wachsmuth_2019, title={Book Review: Argumentation Mining}, volume={45}, number={3}, journal={Computational Linguistics}, publisher={ACL}, author={Wachsmuth, Henning}, year={2019}, pages={603–606} }

CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction

G. Skitalinskaya, J. Klaﬀ, M. Spliethöver, 2019

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In this work we describe our results achieved in the ProtestNews Lab at CLEF 2019. To tackle the problems of event sentence detection and event extraction we decided to use contextualized string embeddings. The models were trained on a data corpus collected from Indian news sources, but evaluated on data obtained from news sources from other countries as well, such as China. Our models have obtained competitive results and have scored 3rd in the event sentence detection task and 1st in the event extraction task based on average F1-scores for diﬀerent test datasets.

@book{Skitalinskaya_Klaﬀ_Spliethöver_2019, place={Lugano, Switzerland}, series={CEUR Workshop Proceedings}, title={CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction}, volume={2380}, author={Skitalinskaya, Gabriella and Klaﬀ, Jonas and Spliethöver, Maximilian}, year={2019}, collection={CEUR Workshop Proceedings} }

http://ceur-ws.org/Vol-2380/paper_118.pdf

Computational Argumentation Synthesis as a Language Modeling Task

R. El Baff, H. Wachsmuth, K. Al-Khatib, M. Stede, B. Stein, in: Proceedings of the 12th International Conference on Natural Language Generation, Association for Computational Linguistics, 2019, pp. 54-64

Citation (Bibtex)

@inproceedings{El Baff_Wachsmuth_Al-Khatib_Stede_Stein_2019, title={Computational Argumentation Synthesis as a Language Modeling Task}, booktitle={Proceedings of the 12th International Conference on Natural Language Generation}, publisher={Association for Computational Linguistics}, author={El Baff, Roxanne and Wachsmuth, Henning and Al-Khatib, Khalid and Stede, Manfred and Stein, Benno}, year={2019}, pages={54–64} }

Data Acquisition for Argument Search: The args.me Corpus

Y. Ajjour, H. Wachsmuth, J. Kiesel, M. Potthast, M. Hagen, B. Stein, in: Proceedings of the 42nd Edition of the German Conference on Artificial Intelligence, 2019, pp. 48-59

Citation (Bibtex)

@inproceedings{Ajjour_Wachsmuth_ Kiesel_Potthast_Hagen_Stein_2019, title={Data Acquisition for Argument Search: The args.me Corpus}, booktitle={Proceedings of the 42nd Edition of the German Conference on Artificial Intelligence}, author={Ajjour, Yamen and Wachsmuth, Henning and Kiesel, Johannes and Potthast, Martin and Hagen, Matthias and Stein, Benno}, year={2019}, pages={48–59} }

Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

M. Spliethöver, J. Klaff, H. Heuer, in: Proceedings of the 6th Workshop on Argument Mining, Association for Computational Linguistics, 2019, pp. 74-82

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Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new state-of-the-art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing. With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach for the unit segmentation task. We also compare sentence-level contextualized word embeddings to pre-generated ones. Our findings suggest that for this task, the additional attention layer does not improve the performance. In most cases, contextualized embeddings do also not show an improvement on the score achieved by pre-defined embeddings.

@inproceedings{Spliethöver_Klaff_Heuer_2019, place={Florence, Italy}, title={Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation}, DOI={10.18653/v1/W19-4509}, booktitle={Proceedings of the 6th Workshop on Argument Mining}, publisher={Association for Computational Linguistics}, author={Spliethöver, Maximilian and Klaff, Jonas and Heuer, Hendrik}, year={2019}, pages={74–82} }

https://www.aclweb.org/anthology/W19-4509/

Modeling Frames in Argumentation

Y. Ajjour, M. Alshomary, H. Wachsmuth, B. Stein, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, 2019, pp. 2915 - 2925

Citation (Bibtex)

@inproceedings{Ajjour_Alshomary_Wachsmuth_Stein_2019, title={Modeling Frames in Argumentation}, booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}, author={Ajjour, Yamen and Alshomary, Milad and Wachsmuth, Henning and Stein, Benno}, year={2019}, pages={2915–2925} }

Proceedings of the 6th Workshop on Argument Mining

B. Stein, H. Wachsmuth, Association for Computational Linguistics, 2019

A Plan for Ancillary Copyright: Original Snippets.

M. Potthast, W. Chen, M. Hagen, B. Stein, in: Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval, 2018, pp. 3-5

Citation (Bibtex)

@inproceedings{Potthast_Chen_Hagen_Stein_2018, title={A Plan for Ancillary Copyright: Original Snippets.}, booktitle={Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval}, author={Potthast, Martin and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno}, year={2018}, pages={3–5} }

A User Study on Snippet Generation: Text Reuse vs. Paraphrases

W. Chen, M. Hagen, B. Stein, M. Potthast, in: Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018, pp. 1033-1036

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@inproceedings{Chen_Hagen_Stein_Potthast_2018, title={A User Study on Snippet Generation: Text Reuse vs. Paraphrases}, booktitle={Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval}, author={Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Potthast, Martin}, year={2018}, pages={1033–1036} }

Application of Sentiment Analysis to Language Learning

M. Chen, W. Chen, L. Ku, IEEE Access (2018), 6, pp. 24433-24442

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@article{Chen_Chen_Ku_2018, title={Application of Sentiment Analysis to Language Learning}, volume={6}, journal={IEEE Access}, publisher={IEEE}, author={Chen, Mei-Hua and Chen, Wei-Fan and Ku, Lun-Wei}, year={2018}, pages={24433–24442} }

Argumentation Synthesis following Rhetorical Strategies

H. Wachsmuth, M. Stede, R. El Baff, K. Al Khatib, M. Skeppstedt, B. Stein, in: Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 3753-3765

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@inproceedings{Wachsmuth_Stede_El Baff_Al Khatib_Skeppstedt_Stein_2018, title={Argumentation Synthesis following Rhetorical Strategies}, booktitle={Proceedings of the 27th International Conference on Computational Linguistics}, author={Wachsmuth, Henning and Stede, Manfred and El Baff, Roxanne and Al Khatib, Khalid and Skeppstedt, Maria and Stein, Benno}, year={2018}, pages={3753–3765} }

Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation

I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, pp. 386-396

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@inproceedings{Habernal_Wachsmuth_Gurevych_Stein_2018, title={Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation}, booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}, author={Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}, year={2018}, pages={386–396} }

Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus

R. El Baff, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the 22nd Conference on Computational Natural Language Learning, Association for Computational Linguistics, 2018, pp. 454-464

Citation (Bibtex)

@inproceedings{El Baff_Wachsmuth_Al Khatib_Stein_2018, title={Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus}, booktitle={Proceedings of the 22nd Conference on Computational Natural Language Learning}, publisher={Association for Computational Linguistics}, author={El Baff, Roxanne and Wachsmuth, Henning and Al Khatib, Khalid and Stein, Benno}, year={2018}, pages={454–464} }

If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands

M. Bonfert, M. Spliethöver, R. Arzaroli, M. Lange, M. Hanci, R. Porzel, in: Proceedings of the 20th ACM International Conference on Multimodal Interaction, 2018

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@inproceedings{Bonfert_Spliethöver_Arzaroli_Lange_Hanci_Porzel_2018, title={If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands}, DOI={10.1145/3242969.3242995}, booktitle={Proceedings of the 20th ACM International Conference on Multimodal Interaction}, author={Bonfert, Michael and Spliethöver, Maximilian and Arzaroli, Roman and Lange, Marvin and Hanci, Martin and Porzel, Robert}, year={2018} }

Learning to Flip the Bias of News Headlines

W. Chen, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the 11th International Conference on Natural Language Generation, Association for Computational Linguistics, 2018, pp. 79-88

Citation (Bibtex)

@inproceedings{Chen_Wachsmuth_Al Khatib_Stein_2018, title={Learning to Flip the Bias of News Headlines}, booktitle={Proceedings of the 11th International Conference on Natural Language Generation}, publisher={Association for Computational Linguistics}, author={Chen, Wei-Fan and Wachsmuth, Henning and Al Khatib, Khalid and Stein, Benno}, year={2018}, pages={79–88} }

Modeling Deliberative Argumentation Strategies on Wikipedia

K. Al Khatib, H. Wachsmuth, K. Lang, J. Herpel, M. Hagen, B. Stein, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 2545-2555

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@inproceedings{Al Khatib_Wachsmuth_Lang_Herpel_Hagen_Stein_2018, title={Modeling Deliberative Argumentation Strategies on Wikipedia}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, author={Al Khatib, Khalid and Wachsmuth, Henning and Lang, Kevin and Herpel, Jakob and Hagen, Matthias and Stein, Benno}, year={2018}, pages={2545–2555} }

Reproducible Web Corpora

J. Kiesel, F. Kneist, M. Alshomary, B. Stein, M. Hagen, M. Potthast, Journal of Data and Information Quality (2018), pp. 1-25

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@article{Kiesel_Kneist_Alshomary_Stein_Hagen_Potthast_2018, title={Reproducible Web Corpora}, DOI={10.1145/3239574}, journal={Journal of Data and Information Quality}, author={Kiesel, Johannes and Kneist, Florian and Alshomary, Milad and Stein, Benno and Hagen, Matthias and Potthast, Martin}, year={2018}, pages={1–25} }

Retrieval of the Best Counterargument without Prior Topic Knowledge

H. Wachsmuth, S. Syed, B. Stein, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 241-251

Citation (Bibtex)

@inproceedings{Wachsmuth_Syed_Stein_2018, title={Retrieval of the Best Counterargument without Prior Topic Knowledge}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, author={Wachsmuth, Henning and Syed, Shahbaz and Stein, Benno}, year={2018}, pages={241–251} }

SemEval-2018 Task 12: The Argument Reasoning Comprehension Task

I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of The 12th International Workshop on Semantic Evaluation, 2018, pp. 763-772

Citation (Bibtex)

@inproceedings{Habernal_Wachsmuth_Gurevych_Stein_2018, title={SemEval-2018 Task 12: The Argument Reasoning Comprehension Task}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, author={Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}, year={2018}, pages={763–772} }

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018, pp. 1930–1940

Citation (Bibtex)

@inproceedings{Habernal_Wachsmuth_Gurevych_Stein_2018, title={The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants}, booktitle={Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, author={Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna and Stein, Benno}, year={2018}, pages={1930–1940} }

"Page Rank'' for Argument Relevance

H. Wachsmuth, B. Stein, Y. Ajjour, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 1117-1127

Citation (Bibtex)

@inproceedings{Wachsmuth_Stein_Ajjour_2017, title={“Page Rank’’’ for Argument Relevance”}, booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers}, author={Wachsmuth, Henning and Stein, Benno and Ajjour, Yamen}, year={2017}, pages={1117–1127} }

A Universal Model for Discourse-Level Argumentation Analysis

H. Wachsmuth, B. Stein, Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (2017)(3), pp. 1-24

Citation (Bibtex)

@article{Wachsmuth_Stein_2017, title={A Universal Model for Discourse-Level Argumentation Analysis}, number={3}, journal={Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media}, author={Wachsmuth, Henning and Stein, Benno}, year={2017}, pages={1–24} }

Argumentation Quality Assessment: Theory vs. Practice

H. Wachsmuth, N. Naderi, I. Habernal, Y. Hou, G. Hirst, I. Gurevych, B. Stein, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017, pp. 250-255

Citation (Bibtex)

DOI

@inproceedings{Wachsmuth_Naderi_Habernal_Hou_Hirst_Gurevych_Stein_2017, title={Argumentation Quality Assessment: Theory vs. Practice}, DOI={10.18653/v1/P17-2039}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, author={Wachsmuth, Henning and Naderi, Nona and Habernal, Ivan and Hou, Yufang and Hirst, Graeme and Gurevych, Iryna and Stein, Benno}, year={2017}, pages={250–255} }

Building an Argument Search Engine for the Web

H. Wachsmuth, M. Potthast, K. Al-Khatib, Y. Ajjour, J. Puschmann, J. Qu, J. Dorsch, V. Morari, J. Bevendorff, B. Stein, in: Proceedings of the 4th Workshop on Argument Mining, 2017, pp. 49-59

Citation (Bibtex)

@inproceedings{Wachsmuth_Potthast_Al-Khatib_Ajjour_Puschmann_Qu_Dorsch_Morari_Bevendorff_Stein_2017, title={Building an Argument Search Engine for the Web}, booktitle={Proceedings of the 4th Workshop on Argument Mining}, author={Wachsmuth, Henning and Potthast, Martin and Al-Khatib, Khalid and Ajjour, Yamen and Puschmann, Jana and Qu, Jiani and Dorsch, Jonas and Morari, Viorel and Bevendorff, Janek and Stein, Benno}, year={2017}, pages={49–59} }

Computational Argumentation Quality Assessment in Natural Language

H. Wachsmuth, N. Naderi, Y. Hou, Y. Bilu, V. Prabhakaran, T.A. Thijm, G. Hirst, B. Stein, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 176-187

Citation (Bibtex)

@inproceedings{Wachsmuth_Naderi_Hou_Bilu_Prabhakaran_Thijm_Hirst_Stein_2017, title={Computational Argumentation Quality Assessment in Natural Language}, booktitle={Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers}, author={Wachsmuth, Henning and Naderi, Nona and Hou, Yufang and Bilu, Yonatan and Prabhakaran, Vinodkumar and Thijm, Tim Alberdingk and Hirst, Graeme and Stein, Benno}, year={2017}, pages={176–187} }

How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement

W. Chen, Y. Chen, L. Ku, in: International Conference on HCI in Business, Government, and Organizations, 2017, pp. 190-202

Citation (Bibtex)

@inproceedings{Chen_Chen_Ku_2017, title={How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement}, booktitle={International Conference on HCI in Business, Government, and Organizations}, author={Chen, Wei-Fan and Chen, Yi-Pei and Ku, Lun-Wei}, year={2017}, pages={190–202} }

Patterns of Argumentation Strategies across Topics

K. Al Khatib, H. Wachsmuth, M. Hagen, B. Stein, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 1362-1368

Citation (Bibtex)

@inproceedings{Al Khatib_Wachsmuth_Hagen_Stein_2017, title={Patterns of Argumentation Strategies across Topics}, booktitle={Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing}, author={Al Khatib, Khalid and Wachsmuth, Henning and Hagen, Matthias and Stein, Benno}, year={2017}, pages={1362–1368} }

The Impact of Modeling Overall Argumentation with Tree Kernels

H. Wachsmuth, G. Da San Martino, D. Kiesel, B. Stein, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 2369-2379

Citation (Bibtex)

@inproceedings{Wachsmuth_Da San Martino_Kiesel_Stein_2017, title={The Impact of Modeling Overall Argumentation with Tree Kernels}, booktitle={Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing}, author={Wachsmuth, Henning and Da San Martino, Giovanni and Kiesel, Dora and Stein, Benno}, year={2017}, pages={2369–2379} }

A News Editorial Corpus for Mining Argumentation Strategies

K. Al Khatib, H. Wachsmuth, J. Kiesel, M. Hagen, B. Stein, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 3433-3443

Citation (Bibtex)

@inproceedings{Al Khatib_Wachsmuth_Kiesel_Hagen_Stein_2016, title={A News Editorial Corpus for Mining Argumentation Strategies}, booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers}, author={Al Khatib, Khalid and Wachsmuth, Henning and Kiesel, Johannes and Hagen, Matthias and Stein, Benno}, year={2016}, pages={3433–3443} }

Chinese Textual Sentiment Analysis: Datasets, Resources and Tools

L. Ku, W. Chen. Chinese Textual Sentiment Analysis: Datasets, Resources and Tools. In: , 2016.

Citation (Bibtex)

@inproceedings{Ku_Chen_2016, title={Chinese Textual Sentiment Analysis: Datasets, Resources and Tools}, booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts}, author={Ku, Lun-Wei and Chen, Wei-Fan}, year={2016}, pages={5–8} }

Cross-Domain Mining of Argumentative Text through Distant Supervision

K. Al-Khatib, H. Wachsmuth, M. Hagen, J. Köhler, B. Stein, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016, pp. 1395-1404

Citation (Bibtex)

DOI

@inproceedings{Al-Khatib_Wachsmuth_Hagen_Köhler_Stein_2016, title={Cross-Domain Mining of Argumentative Text through Distant Supervision}, DOI={10.18653/v1/N16-1165}, booktitle={Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, author={Al-Khatib, Khalid and Wachsmuth, Henning and Hagen, Matthias and Köhler, Jonas and Stein, Benno}, year={2016}, pages={1395–1404} }

Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse

H. Wachsmuth, in: Ausgezeichnete Informatikdissertationen 2015, 2016, pp. 329-338

Citation (Bibtex)

@inproceedings{Wachsmuth_2016, title={Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse}, booktitle={Ausgezeichnete Informatikdissertationen 2015}, author={Wachsmuth, Henning}, year={2016}, pages={329–338} }

A Computer-assistance Learning System for Emotional Wording

W. Chen, M. Chen, M. Chen, L. Ku, IEEE Transactions on Knowledge and Data Engineering (2015), 28(5), pp. 1093-1104

Citation (Bibtex)

@article{Chen_Chen_Chen_Ku_2015, title={A Computer-assistance Learning System for Emotional Wording}, volume={28}, number={5}, journal={IEEE Transactions on Knowledge and Data Engineering}, publisher={IEEE}, author={Chen, Wei-Fan and Chen, Mei-Hua and Chen, Ming-Lung and Ku, Lun-Wei}, year={2015}, pages={1093–1104} }

Embarrassed or Awkward? Ranking Emotion Synonyms for ESL Learners’ Appropriate Wording

W. Chen, M. Chen, L. Ku, in: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 144-153

Citation (Bibtex)

@inproceedings{Chen_Chen_Ku_2015, title={Embarrassed or Awkward? Ranking Emotion Synonyms for ESL Learners’ Appropriate Wording}, booktitle={Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications}, author={Chen, Wei-Fan and Chen, MeiHua and Ku, Lun-Wei}, year={2015}, pages={144–153} }

Mining Supportive and Unsupportive Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example

W. Chen, L. Ku, Y. Lee, in: 2015 AAAI Spring Symposium Series, 2015

Citation (Bibtex)

@inproceedings{Chen_Ku_Lee_2015, title={Mining Supportive and Unsupportive Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example}, booktitle={2015 AAAI Spring Symposium Series}, author={Chen, Wei-Fan and Ku, Lun-Wei and Lee, Yann-Hui}, year={2015} }

Pipelines for Ad-hoc Large-scale Text Mining

H. Wachsmuth, 2015

Abstract

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Today's web search and big data analytics applications aim to address information needs~(typically given in the form of search queries) ad-hoc on large numbers of texts. In order to directly return relevant information instead of only returning potentially relevant texts, these applications have begun to employ text mining. The term text mining covers tasks that deal with the inference of structured high-quality information from collections and streams of unstructured input texts. Text mining requires task-specific text analysis processes that may consist of several interdependent steps. These processes are realized with sequences of algorithms from information extraction, text classification, and natural language processing. However, the use of such text analysis pipelines is still restricted to addressing a few predefined information needs. We argue that the reasons behind are three-fold: First, text analysis pipelines are usually made manually in respect of the given information need and input texts, because their design requires expert knowledge about the algorithms to be employed. When information needs have to be addressed that are unknown beforehand, text mining hence cannot be performed ad-hoc. Second, text analysis pipelines tend to be inefficient in terms of run-time, because their execution often includes analyzing texts with computationally expensive algorithms. When information needs have to be addressed ad-hoc, text mining hence cannot be performed in the large. And third, text analysis pipelines tend not to robustly achieve high effectiveness on all texts, because their results are often inferred by algorithms that rely on domain-dependent features of texts. Hence, text mining currently cannot guarantee to infer high-quality information. In this thesis, we contribute to the question of how to address information needs from text mining ad-hoc in an efficient and domain-robust manner. We observe that knowledge about a text analysis process and information obtained within the process help to improve the design, the execution, and the results of the pipeline that realizes the process. To this end, we apply different techniques from classical and statistical artificial intelligence. In particular, we first develop knowledge-based approaches for an ad-hoc pipeline construction and for an optimal execution of a pipeline on its input. Then, we show theoretically and practically how to optimize and adapt the schedule of the algorithms in a pipeline based on information in the analyzed input texts in order to maximize execution efficiency. Finally, we learn patterns in the argumentation structures of texts statistically that remain strongly invariant across domains and that, thereby, allow for more robust analysis results in a restricted set of tasks. We formally analyze all developed approaches and we implement them as open-source software applications. Based on these applications, we evaluate the approaches on established and on newly created collections of texts for scientifically and industrially important text analysis tasks, such as financial event extraction and fine-grained sentiment analysis. Our findings show that text analysis pipelines can be designed automatically, which process only portions of text that are relevant for the information need at hand. Through scheduling, the run-time efficiency of pipelines can be improved by up to more than one order of magnitude while maintaining effectiveness. Moreover, we provide evidence that a pipeline's domain robustness substantially benefits from focusing on argumentation structure in tasks like sentiment analysis. We conclude that our approaches denote essential building blocks of enabling ad-hoc large-scale text mining in web search and big data analytics applications.

@book{Wachsmuth_2015, title={Pipelines for Ad-hoc Large-scale Text Mining}, author={Wachsmuth, Henning}, year={2015} }

https://webis.de/downloads/publications/papers/wachsmuth_2013.pdf - (confirmation)

Sentiment Flow - A General Model of Web Review Argumentation

H. Wachsmuth, J. Kiesel, B. Stein, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 601-611

Citation (Bibtex)

DOI

@inproceedings{Wachsmuth_Kiesel_Stein_2015, series={Lecture Notes in Computer Science}, title={Sentiment Flow - A General Model of Web Review Argumentation}, DOI={10.18653/v1/D15-1072}, booktitle={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, author={Wachsmuth, Henning and Kiesel, Johannes and Stein, Benno}, editor={Tsujii, Junichi and Hajic, JanEditors}, year={2015}, pages={601–611}, collection={Lecture Notes in Computer Science} }

Technology Enhanced Emotion Expression Learning

M. Chen, W. Chen, L. Ku, in: Proceedings of the sixth joint Foreign Language Education and Technology Conference (FLEAT VI), 2015

Citation (Bibtex)

@inproceedings{Chen_Chen_Ku_2015, title={Technology Enhanced Emotion Expression Learning}, booktitle={Proceedings of the sixth joint Foreign Language Education and Technology Conference (FLEAT VI)}, author={Chen, Mei-Hua and Chen, Wei-Fan and Ku, Lun-Wei}, year={2015} }

Text Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining

H. Wachsmuth, 2015

Citation (Bibtex)

DOI

@book{Wachsmuth_2015, series={Lecture Notes in Computer Science}, title={Text Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining}, DOI={http://dx.doi.org/10.1007/978-3-319-25741-9}, author={Wachsmuth, Henning}, year={2015}, collection={Lecture Notes in Computer Science} }

A Review Corpus for Argumentation Analysis

H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, T. Palakarska, in: Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics, 2014, pp. 115–127

Citation (Bibtex)

@inproceedings{Wachsmuth_Trenkmann_Stein_Engels_Palakarska_2014, title={A Review Corpus for Argumentation Analysis}, booktitle={Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics}, author={Wachsmuth, Henning and Trenkmann, Martin and Stein, Benno and Engels, Gregor and Palakarska, Tsvetomira}, year={2014}, pages={115–127} }

iSoNTRE: The Social Network Transformer into Recommendation Engine

C. Abu Quba Rana, S. Hassas, F. Usama, M. Alshomary, C. Gertosio, 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) (2014), pp. 169-175

Citation (Bibtex)

@article{Abu Quba Rana_Hassas_Usama_Alshomary_Gertosio_2014, title={iSoNTRE: The Social Network Transformer into Recommendation Engine}, journal={2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)}, author={Abu Quba Rana, Chamsi and Hassas, Salima and Usama, Fayyad and Alshomary, Milad and Gertosio, Christine}, year={2014}, pages={169–175} }

Modeling Review Argumentation for Robust Sentiment Analysis

H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, in: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014, pp. 553-564

Citation (Bibtex)

@inproceedings{Wachsmuth_Trenkmann_Stein_Engels_2014, title={Modeling Review Argumentation for Robust Sentiment Analysis}, booktitle={Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers}, author={Wachsmuth, Henning and Trenkmann, Martin and Stein, Benno and Engels, Gregor}, year={2014}, pages={553–564} }

PBlaman: performance blame analysis based on Palladio contracts

F. Brüseke, H. Wachsmuth, G. Engels, S. Becker, in: Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment, 2014, pp. 1975-2004

Citation (Bibtex)

@inproceedings{Brüseke_Wachsmuth_Engels_Becker_2014, title={PBlaman: performance blame analysis based on Palladio contracts}, number={12}, booktitle={Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment}, author={Brüseke, Frank and Wachsmuth, Henning and Engels, Gregor and Becker, Steffen}, year={2014}, pages={1975–2004} }

RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions

M. Chen, W. Chen, L. Ku, in: Proceedings of the AsiaCALL 2014, 2014

Citation (Bibtex)

@inproceedings{Chen_Chen_ Ku_2014, title={RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions}, booktitle={Proceedings of the AsiaCALL 2014}, author={Chen, Mei-Hua and Chen, Wei-Fan and Ku, Lun-Wei}, year={2014} }

Automatic Pipeline Construction for Real-Time Annotation

H. Wachsmuth, M. Rose, G. Engels, in: 14th International Conference on Intelligent Text Processing and Computational Linguistics, 2013, pp. 38-49

Citation (Bibtex)

@inproceedings{Wachsmuth_Rose_Engels_2013, series={Lecture Notes in Computer Science}, title={Automatic Pipeline Construction for Real-Time Annotation}, booktitle={14th International Conference on Intelligent Text Processing and Computational Linguistics}, author={Wachsmuth, Henning and Rose, Mirko and Engels, Gregor}, editor={Gelbukh, AlexanderEditor}, year={2013}, pages={38–49}, collection={Lecture Notes in Computer Science} }

Information Extraction as a Filtering Task

H. Wachsmuth, B. Stein, G. Engels, in: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, 2013, pp. 2049-2058

Citation (Bibtex)

@inproceedings{Wachsmuth_Stein_Engels_2013, title={Information Extraction as a Filtering Task}, booktitle={Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management}, author={Wachsmuth, Henning and Stein, Benno and Engels, Gregor}, year={2013}, pages={2049–2058} }

Learning Efficient Information Extraction on Heterogeneous Texts

H. Wachsmuth, B. Stein, G. Engels, in: Proceedings of the Sixth International Joint Conference on Natural Language Processing, 2013, pp. 534-542

Citation (Bibtex)

@inproceedings{Wachsmuth_Stein_Engels_2013, series={Lecture Notes in Computer Science}, title={Learning Efficient Information Extraction on Heterogeneous Texts}, booktitle={Proceedings of the Sixth International Joint Conference on Natural Language Processing}, author={Wachsmuth, Henning and Stein, Benno and Engels, Gregor}, editor={Gelbukh, AlexanderEditor}, year={2013}, pages={534–542}, collection={Lecture Notes in Computer Science} }

A Syllable-based Prosody Modeling for L1 and L2 English Speeches

W. Chen, C. Kuo, Y. Wang, S. Chen, in: Proceedings of the 8th International Symposium on Chinese Spoken Language Processing, IEEE, 2012, pp. 281-285

Citation (Bibtex)

@inproceedings{Chen_Kuo_ Wang_ Chen_2012, title={A Syllable-based Prosody Modeling for L1 and L2 English Speeches}, booktitle={Proceedings of the 8th International Symposium on Chinese Spoken Language Processing}, publisher={IEEE}, author={Chen, Wei-Fan and Kuo, Chin-Kuan and Wang, Yih-Ru and Chen, Sin-Horng}, year={2012}, pages={281–285} }

Optimal Scheduling of Information Extraction Algorithms

H. Wachsmuth, B. Stein, in: Proceedings of COLING 2012: Posters, 2012, pp. 1281-1290

Citation (Bibtex)

@inproceedings{Wachsmuth_Stein_2012, title={Optimal Scheduling of Information Extraction Algorithms}, booktitle={Proceedings of COLING 2012: Posters}, author={Wachsmuth, Henning and Stein, Benno}, year={2012}, pages={1281–1290} }

Back to the Roots of Genres: Text Classification by Language Function

H. Wachsmuth, K. Bujna, in: Proceedings of 5th International Joint Conference on Natural Language Processing, 2011, pp. 632-640

Citation (Bibtex)

@inproceedings{Wachsmuth_Bujna_2011, title={Back to the Roots of Genres: Text Classification by Language Function}, booktitle={Proceedings of 5th International Joint Conference on Natural Language Processing}, author={Wachsmuth, Henning and Bujna, Kathrin}, editor={Berendt, Bettina and de Vries, Arjen and Fan, Wenfei and Macdonald, Craig and Ounis, Iadh and Ruthven, IanEditors}, year={2011}, pages={632–640} }

Constructing Efficient Information Extraction Pipelines

H. Wachsmuth, B. Stein, G. Engels, in: 20th ACM International Conference on Information and Knowledge Management, 2011, pp. 2237-2240

Citation (Bibtex)

@inproceedings{Wachsmuth_Stein_Engels_2011, title={Constructing Efficient Information Extraction Pipelines}, booktitle={20th ACM International Conference on Information and Knowledge Management}, author={Wachsmuth, Henning and Stein, Benno and Engels, Gregor}, editor={Berendt, Bettina and de Vries, Arjen and Fan, Wenfei and Macdonald, Craig and Ounis, Iadh and Ruthven, IanEditors}, year={2011}, pages={2237–2240} }

Efficient Statement Identification for Automatic Market Forecasting

H. Wachsmuth, P. Prettenhofer, B. Stein, in: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), 2010, pp. 1128-1136

Citation (Bibtex)

@inproceedings{Wachsmuth_Prettenhofer_Stein_2010, title={Efficient Statement Identification for Automatic Market Forecasting}, booktitle={Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)}, author={Wachsmuth, Henning and Prettenhofer, Peter and Stein, Benno}, year={2010}, pages={1128–1136} }

Smart Teams: Simulating Large Robotic Swarms in Vast Environments

S. Arens, A. Buss, H. Deck, M. Dynia, M. Fischer, H. Hagedorn, P. Isaak, J. Kutylowski, F. Meyer auf der Heide, V. Nesterow, A. Ogiermann, B. Stobbe, T. Storm, H. Wachsmuth, in: Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment, Heinz Nixdorf Institut, University of Paderborn, 2007, pp. 215-222

Abstract

Citation (Bibtex)

We consider the problem of exploring an unknown environment using a swarm of autonomous robots with collective behavior emerging from their local rules. Each robot has only a very restricted view on the environment which makes cooperation difficult. We introduce a software system which is capable of simulating a large number of such robots (e.g. 1000) on highly complex terrains with millions of obstacles. Its main purpose is to easily integrate and evaluate any kind of algorithm for controlling the robot behavior. The simulation may be observed in real-time via a visualization that displays both the individual and the collective progress of the robots. We present the system design, its main features and underlying concepts.

@inproceedings{Arens_Buss_Deck_Dynia_Fischer_Hagedorn_Isaak_Kutylowski_Meyer auf der Heide_Nesterow_et al._2007, place={Buenos Aires, Argentina}, title={Smart Teams: Simulating Large Robotic Swarms in Vast Environments}, booktitle={Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment}, publisher={Heinz Nixdorf Institut, University of Paderborn}, author={Arens, Stephan and Buss, Alexander and Deck, Helena and Dynia, Miroslaw and Fischer, Matthias and Hagedorn, Holger and Isaak, Peter and Kutylowski, Jaroslaw and Meyer auf der Heide, Friedhelm and Nesterow, Viktor and et al.}, year={2007}, pages={215–222} }

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