Papers by Christoph Weisser

2 papers
Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class (2024.lrec-main)

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Challenge: Few-shot methods for accurate modeling under sparse label-settings are still challenging in document classification.
Approach: They propose to combine supervised few-shot learning with a topic extraction method to generate coherent topics in large text corpora.
Outcome: The proposed method outperforms unsupervised topic modeling methods in document classification.
STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module (2024.acl-short)

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Challenge: Topic modeling is a widely used technique to analyze large document corpora.
Approach: They propose a module for topic retrieval, exploration, and analysis that implements multiple intruder-word based topic evaluation metrics.
Outcome: The proposed module implements multiple intruder-word based topic evaluation metrics and extends existing datasets.

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