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Multi-Touch-Tisch aus dem Institut für Informatik, Foto: Universität Paderborn, Fotografin: Judith Kraft
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Multi-Touch-Tisch aus dem Institut für Informatik, Foto: Universität Paderborn, Fotografin: Judith Kraft

Jun.-Prof. Dr. Henning Wachsmuth

Kontakt
Publikationen
Jun.-Prof. Dr. Henning Wachsmuth

Social Media in soziotechnischen Systemen

Leiter - Juniorprofessor - Computational Social Science

Telefon:
+49 5251 60-6844
Büro:
ZM2.A.03.02
Web:
Besucher:
Zukunftsmeile 2
33102 Paderborn

Liste im Research Information System öffnen

2022

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.


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


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


"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


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


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


2021

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


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


What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing

Z. Nouri, U. Gadiraju, G. Engels, H. Wachsmuth, in: Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 2021, pp. 165-175


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

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.


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


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


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


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


Toward Audience-aware Argument Generation

M. Alshomary, H. Wachsmuth, Patterns (2021), 2(6)


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


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

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.


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


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

DOI


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


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


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

DOI


2020

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


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


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


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


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

DOI


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


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


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


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


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


Touché: First Shared Task on Argument Retrieval

A. Bondarenko, M. Hagen, M. Potthast, H. Wachsmuth, M. Beloucif, C. Biemann, A. Panchenko, B. Stein, in: Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020), 2020, pp. 517-523


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


Visual Analysis of Argumentation in Essays

D. Kiesel, P. Riehmann, H. Wachsmuth, B. Stein, B. Fröhlich, IEEE Transactions of Visualization & Computer Graphics (2020), 27(2), pp. 1139-1148


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


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


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


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


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.N. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P. Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems (2020), pp. 1-1

DOI


2019

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

DOI


Book Review: Argumentation Mining

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


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


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


Proceedings of the 6th Workshop on Argument Mining

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


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


Wikipedia Text Reuse: Within and Without

M. Alshomary, M. Völske, T. Licht, H. Wachsmuth, B. Stein, M. Hagen, M. Potthast, in: Advances in Information Retrieval, Springer International Publishing, 2019, pp. 747-754

We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample of the Common Crawl). To discover reuse beyond verbatim copy and paste, we employ state-of-the-art text reuse detection technology, scaling it for the first time to process the entire Wikipedia as part of a distributed retrieval pipeline. We further report on a pilot analysis of the 100 million reuse cases inside, and the 1.6 million reuse cases outside Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new tasks such as article template induction, fixing quality flaws, or complementing Wikipedia's ontology. Text reuse outside Wikipedia yields a tangible metric for the emerging field of quantifying Wikipedia's influence on the web. To foster future research into these tasks, and for reproducibility's sake, the Wikipedia text reuse corpus and the retrieval pipeline are made freely available.


Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition

W. Chen, K. Al-Khatib, M. Hagen, H. Wachsmuth, B. Stein, in: Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom, 2019, pp. 76-82


2018

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


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


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


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


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


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


Visualization of the Topic Space of Argument Search Results in args. me

Y. Ajjour, H. Wachsmuth, D. Kiesel, P. Riehmann, F. Fan, G. Castiglia, R. Adejoh, B. Fröhlich, B. Stein, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2018, pp. 60-65


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


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


2017

Unit Segmentation of Argumentative Texts

Y. Ajjour, W. Chen, J. Kiesel, H. Wachsmuth, B. Stein, in: Proceedings of the 4th Workshop on Argument Mining, 2017, pp. 118-128


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


WAT-SL: A Customizable Web Annotation Tool for Segment Labeling

J. Kiesel, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017, pp. 13-16


"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


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


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


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


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


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

DOI


2016

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


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

DOI


Using Argument Mining to Assess the Argumentation Quality of Essays

H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 1680-1691


Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse

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


2015

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

DOI



Pipelines for Ad-hoc Large-scale Text Mining

H. Wachsmuth, 2015

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.


2014

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


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


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


2013

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


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


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


2012

Optimal Scheduling of Information Extraction Algorithms

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


2011

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


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


2010

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


2007

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

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.


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