Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Julian Risch

I am a Ph.D. student at the Information Systems Group and a member of the HPI Research School. My research focuses on topic modeling and deep learning with applications in the field of text mining, in particular, comment analysis. Further, I am involved in projects on patent classification and book recommendation.

Source code for my publications can be found here and on GitHub.

Contact Information

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: F-2.08

Phone: +49 331 5509 272

Email: Julian Risch

Open Master's Theses

I provide supervision for Master's theses in the area of News Comment Analysis, e.g., Toxic Comment Classification, User Engagement Prediction, Comment Recommendation, and Discussion Summarization/Visualization. Feel free to schedule an informal meeting with me to discuss details of these topics and/or your own ideas.

Teaching

Advised Master's Theses

  • Enriching Document Embeddings With Domain Knowledge
  • Modeling News Commenters for Discussion Recommendation
  • Jointly Learning Document and Label Embeddings for Hierarchically Labeled Text
  • Context-aware Classification of News Comments
  • Quality Management for Online News Comments 

Publications

Challenges for Toxic Comment Classification: An In-Depth Error Analysis

van Aken, Betty; Risch, Julian; Krestel, Ralf; Löser, Alexander in Proceedings of the 2nd Workshop on Abusive Language Online (co-located with EMNLP) Seite 33-42 . 2018 .

Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task’s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of- the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.
Weitere Informationen
Tagscomments_analysis  isg  myown  web_science