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Die Universität im Winter mit Blick auf den Turm vom J-Gebäude. Bildinformationen anzeigen

Die Universität im Winter mit Blick auf den Turm vom J-Gebäude.

Foto: Universität Paderborn, Adelheid Rutenburges

Jun. Prof. Dr. Henning Wachsmuth

Kontakt
Publikationen
Jun. Prof. Dr. Henning Wachsmuth

Social Media in soziotechnischen Systemen

Leiter - Juniorprofessor - Computational Social Science

Digitale Zukunft

Mitglied - Juniorprofessor

Telefon:
+49 5251 60-6844
Büro:
FU.327
Web:
Besucher:
Fürstenallee 11
33102 Paderborn


Liste im Research Information System öffnen

2019

Data Acquisition for Argument Search: The args.me Corpus.

Y. Ajjour, H. Wachsmuth, J. Kiesel, M. Potthast, M. Hagen, B. Stein, 2019


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


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

DOI


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


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, 2019


Book Review: Argumentation Mining

H. Wachsmuth, Computational Linguistics, ACL (2019)


Wikipedia Text Reuse: Within and Without

M. Alshomary, M. Völske, T. Licht, H. Wachsmuth, B. Stein, M. Hagen, M. Potthast, in: Lecture Notes in Computer Science, 2019

DOI


2018

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


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


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


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


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


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


2017

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


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


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


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


"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


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. 28:1--28:24

DOI


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


2016

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


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


2015

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.


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



2014

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


Modeling Review Argumentation for Robust Sentiment Analysis

H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, in: 25th International Conference on Computational Linguistics, 2014, pp. 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


2013

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

DOI


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

DOI


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

DOI


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

DOI


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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