arXiv:2607. 13260v1 Announce Type: cross Abstract: Policy documents shape governance outcomes, but their reasoning is often implicit.
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The paper introduces a structured framework for claim-level discourse analysis in dense health narratives, addressing the limitations of existing topic- or sentiment-based representations. It identifies an average of 13.22 atomic claims per minute in social media health videos and proposes tuples that link each claim to thematic aspects, stance, and multidimensional pragmatic attributes. A benchmark of 1,191 manually annotated claims from 60 videos across four health domains is created, and experiments show that large language models perform well on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, indicating a need for task-specific inference strategies.
arXiv:2601. 13317v2 Announce Type: replace-cross Abstract: Climate discourse online shapes public understanding of climate change and informs political and policy debate, yet it unfolds across structurally different environments: paid advertising platforms host targeted, institutionally produced messaging, while public social media reflects largely organic, user-driven discussion.
Political debates are often analyzed through Argument Mining (AM) to investigate the key arguments that drive them. However, political arguments are rarely interpretable from argumentative spans alone...
The paper introduces a retrieval‑augmented framework for detecting and classifying fallacies in political debate transcripts. By dynamically retrieving documents guided by argumentative relations of support and attack, the method leverages external knowledge to improve performance. Experiments on the ElecDeb60to20 benchmark show significant gains, raising macro‑F1 to 0.864 for detection and 0.725 for classification compared to non‑retrieval baselines.
The paper introduces DNE‑ElecDeb, an enriched version of the USElecDeb dataset that annotates Debate Named Entities (DNEs) in both argumentative and non‑argumentative spans, and defines Debate Named Entity Recognition (DNER) as a new task. It proposes Joint Argument and Entity Tagging (JAET), a generative framework that fine‑tunes decoder‑only LLMs to insert inline argument and entity tags into debate turns while preserving the original transcript. JAET achieves significant improvements in joint AM+DNER performance (+27.3% relative F1 in the untyped setting and +41.9% in the typed setting) over sequential pipelines, and these gains generalize to Persuasive Essays (+26.6% and +52.7%).
arXiv:2606.12186v2 Announce Type: replace Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjec...