Benjamin Schiller
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View article: Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets
Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets Open
Topic-Dependent Argument Mining (TDAM), that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large TDAM da…
View article: Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting Open
Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textua…
View article: Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting Open
Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textua…
View article: Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets
Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets Open
The task of Argument Mining, that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining da…
View article: Focusing Knowledge-based Graph Argument Mining via Topic Modeling
Focusing Knowledge-based Graph Argument Mining via Topic Modeling Open
Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extracting evidence, this paper presents a hybrid model that comb…
View article: Aspect-Controlled Neural Argument Generation
Aspect-Controlled Neural Argument Generation Open
We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argu…
View article: ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario
ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario Open
The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large ra…
View article: Study Program Innovation in the Triple Helix Context: The Case of Cooperative Study Programs at a German University of Applied Sciences
Study Program Innovation in the Triple Helix Context: The Case of Cooperative Study Programs at a German University of Applied Sciences Open
The purpose of this article is to understand how Triple Helix linkages foster study program innovation at the micro-level and how the entrepreneurial university shapes support structures and processes to foster this innovation at the meso-…
View article: Classification and Clustering of Arguments with Contextualized Word Embeddings
Classification and Clustering of Arguments with Contextualized Word Embeddings Open
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and clus…
View article: UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification Open
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale da…
View article: UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification Open
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale da…
View article: Cross-topic Argument Mining from Heterogeneous Sources
Cross-topic Argument Mining from Heterogeneous Sources Open
Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied…
View article: ArgumenText: Searching for Arguments in Heterogeneous Sources
ArgumenText: Searching for Arguments in Heterogeneous Sources Open
Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computati…
View article: Characterization of Surface Quality for Inconel 625 Components Manufactured by Selective Laser Melting
Characterization of Surface Quality for Inconel 625 Components Manufactured by Selective Laser Melting Open
The surface quality of components manufactured by Selective Laser Melting is strongly affected by various process parameters.In this study, we focus on the influence of laser power and scanning speed on the surface roughness of test compon…