Stefan Conrad


2022

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Developing an argument annotation scheme based on a semantic classification of arguments
Lea Kawaletz | Heidrun Dorgeloh | Stefan Conrad | Zeljko Bekcic
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises. Both argument detection and classification are complex discourse processing tasks. Our paper introduces a semantic classification of arguments that can help to facilitate argument detection. We report on our experiences with corpus annotations using a function-based classification of arguments and a procedure for operationalizing the scheme by using semantic templates.

2021

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Combining text and vision in compound semantics: Towards a cognitively plausible multimodal model
Abhijeet Gupta | Fritz Günther | Ingo Plag | Laura Kallmeyer | Stefan Conrad
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

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Citizen Involvement in Urban Planning - How Can Municipalities Be Supported in Evaluating Public Participation Processes for Mobility Transitions?
Julia Romberg | Stefan Conrad
Proceedings of the 8th Workshop on Argument Mining

Public participation processes allow citizens to engage in municipal decision-making processes by expressing their opinions on specific issues. Municipalities often only have limited resources to analyze a possibly large amount of textual contributions that need to be evaluated in a timely and detailed manner. Automated support for the evaluation is therefore essential, e.g. to analyze arguments. In this paper, we address (A) the identification of argumentative discourse units and (B) their classification as major position or premise in German public participation processes. The objective of our work is to make argument mining viable for use in municipalities. We compare different argument mining approaches and develop a generic model that can successfully detect argument structures in different datasets of mobility-related urban planning. We introduce a new data corpus comprising five public participation processes. In our evaluation, we achieve high macro F1 scores (0.76 - 0.80 for the identification of argumentative units; 0.86 - 0.93 for their classification) on all datasets. Additionally, we improve previous results for the classification of argumentative units on a similar German online participation dataset.

2020

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Annotating Patient Information Needs in Online Diabetes Forums
Julia Romberg | Jan Dyczmons | Sandra Olivia Borgmann | Jana Sommer | Markus Vomhof | Cecilia Brunoni | Ismael Bruck-Ramisch | Luis Enders | Andrea Icks | Stefan Conrad
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

Identifying patient information needs is an important issue for health care services and implementation of patient-centered care. A relevant number of people with diabetes mellitus experience a need for information during the course of the disease. Health-related online forums are a promising option for researching relevant information needs closely related to everyday life. In this paper, we present a novel data corpus comprising 4,664 contributions from an online diabetes forum in German language. Two annotation tasks were implemented. First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0.89 (Krippendorff’s α). Moreover, the textual content of diabetes-specific information needs was segmented and labeled using a well-founded definition of health-related information needs, which achieved a promising agreement of 0.82 (Krippendorff’s αu). We further report a baseline for two sub-tasks of the information extraction system planned for the long term: contribution categorization and segment classification.

2019

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HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo
Alexander Oberstrass | Julia Romberg | Anke Stoll | Stefan Conrad
Proceedings of the 13th International Workshop on Semantic Evaluation

We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6). Our results show that context embeddings are important features for the three different sub-tasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro F1 of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.

2018

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HHU at SemEval-2018 Task 12: Analyzing an Ensemble-based Deep Learning Approach for the Argument Mining Task of Choosing the Correct Warrant
Matthias Liebeck | Andreas Funke | Stefan Conrad
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our participation in the SemEval-2018 Task 12 Argument Reasoning Comprehension Task which calls to develop systems that, given a reason and a claim, predict the correct warrant from two opposing options. We decided to use a deep learning architecture and combined 623 models with different hyperparameters into an ensemble. Our extensive analysis of our architecture and ensemble reveals that the decision to use an ensemble was suboptimal. Additionally, we benchmark a support vector machine as a baseline. Furthermore, we experimented with an alternative data split and achieved more stable results.

2017

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HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods
Tobias Cabanski | Julia Romberg | Stefan Conrad
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this Paper a system for solving SemEval-2017 Task 5 is presented. This task is divided into two tracks where the sentiment of microblog messages and news headlines has to be predicted. Since two submissions were allowed, two different machine learning methods were developed to solve this task, a support vector machine approach and a recurrent neural network approach. To feed in data for these approaches, different feature extraction methods are used, mainly word representations and lexica. The best submissions for both tracks are provided by the recurrent neural network which achieves a F1-score of 0.729 in track 1 and 0.702 in track 2.

2016

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HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity
Matthias Liebeck | Philipp Pollack | Pashutan Modaresi | Stefan Conrad
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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What to Do with an Airport? Mining Arguments in the German Online Participation Project Tempelhofer Feld
Matthias Liebeck | Katharina Esau | Stefan Conrad
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

2015

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IWNLP: Inverse Wiktionary for Natural Language Processing
Matthias Liebeck | Stefan Conrad
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2013

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Opinion Mining in Newspaper Articles by Entropy-Based Word Connections
Thomas Scholz | Stefan Conrad
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing