A YOLOv8x, TrOCR, and GLiNER pipeline converts Italian Supreme Court PDFs into an anonymized topic-modeling dataset, but the reported improvement over OCR-only is not supported by the paper's own Table 13.
Evaluation of Thematic Coherence in Microblogs
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.
fields
cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court
A YOLOv8x, TrOCR, and GLiNER pipeline converts Italian Supreme Court PDFs into an anonymized topic-modeling dataset, but the reported improvement over OCR-only is not supported by the paper's own Table 13.