Search Results for author:
Found 19 papers, 12 papers with code
Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain
For the evaluation of the models, we employ a variety of source domain datasets and analyze how each source domain dataset impacts the performance of the different models for our target domain.
Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks
In addition, we hope that jointly modeling semantic change and multiple senses of words results in improvements for the individual tasks.
Data Integration for Toxic Comment Classification: Making More Than 40 Datasets Easily Accessible in One Unified Format
With the rise of research on toxic comment classification, more and more annotated datasets have been released.
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark
On the one hand, the number of published xAI algorithms underwent a boom, and it became difficult for practitioners to select the right tool.
Art Creation with Multi-Conditional StyleGANs
Our approach is based on the StyleGAN neural network architecture, but incorporates a custom multi-conditional control mechanism that provides fine-granular control over characteristics of the generated paintings, e. g., with regard to the perceived emotion evoked in a spectator.
Discovering Fine-Grained Semantics in Knowledge Graph Relations
To do this, we leverage the types of the entities associated with the relations and cluster the vector representations of entities and relations.
Multifaceted Domain-Specific Document Embeddings
Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms.
PatentMatch: A Dataset for Matching Patent Claims & Prior Art
For these reasons, we address the computer-assisted search for prior art by creating a training dataset for supervised machine learning called PatentMatch.
A Dataset of Journalists' Interactions with Their Readership: When Should Article Authors Reply to Reader Comments?
Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i. e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction.
Bagging BERT Models for Robust Aggression Identification
In this paper, we describe such an ensemble system and present our submission to the shared tasks on aggression identification 2020 (team name: Julian).
Offensive Language Detection Explained
However, even with machine-learned models achieving better classification accuracy than human experts, there is still a reason why human moderators are preferred.
Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis
In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives.
Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives.
MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation
Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections
Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis
Toxic comment classification has become an active research field with many recently proposed approaches.
Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom
Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics.
Aggression Identification Using Deep Learning and Data Augmentation
Social media platforms allow users to share and discuss their opinions online.
Prediction for the Newsroom: Which Articles Will Get the Most Comments?
The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time.
