Jonas Rieger

Orcid: 0000-0002-0007-4478

Affiliations:
  • TU Dortmund University, Germany


According to our database1, Jonas Rieger authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
LDAPrototype: a model selection algorithm to improve reliability of latent Dirichlet allocation.
PeerJ Comput. Sci., 2024

Lex2Sent: A bagging approach to unsupervised sentiment analysis.
Proceedings of the 20th Conference on Natural Language Processing, 2024

2023
Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine.
CoRR, 2023

Visually Analyzing Topic Change Points in Temporal Text Collections.
Proceedings of the VMV 2023, 2023

Debunking Disinformation with GADMO: A Topic Modeling Analysis of a Comprehensive Corpus of German-language Fact-Checks.
Proceedings of the 4th Conference on Language, Data and Knowledge, 2023

2022
Reliability evaluation and an update algorithm for the latent Dirichlet allocation.
PhD thesis, 2022

Lex2Sent: A bagging approach to unsupervised sentiment analysis.
CoRR, 2022

SVD-reduction of high-dimensional German spatio-temporal wind speed data and clusters of similarity.
Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Dynamic change detection in topics based on rolling LDAs.
Proceedings of Text2Story, 2022

Finding Scientific Topics in Continuously Growing Text Corpora.
Proceedings of the Third Workshop on Scholarly Document Processing, 2022

2021
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

2020
ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations.
J. Open Source Softw., 2020

Improving Reliability of Latent Dirichlet Allocation by Assessing Its Stability Using Clustering Techniques on Replicated Runs.
CoRR, 2020

Assessing the Uncertainty of the Text Generating Process Using Topic Models.
Proceedings of the ECML PKDD 2020 Workshops, 2020

Improving Latent Dirichlet Allocation: On Reliability of the Novel Method LDAPrototype.
Proceedings of the Natural Language Processing and Information Systems, 2020


  Loading...