🤖 AI Summary
To address the scarcity of high-quality opinion role labeling (ORL) data for opinion mining in low-resource settings, this paper proposes a novel ORL data construction method grounded in semantic role labeling (SRL). We systematically map PropBank-style SRL annotations onto a Holder-Expression-Target ternary schema—the first such effort—and introduce three principled strategies: syntactic tree pointer conversion, discontinuous constituent handling, and semantic consistency filtering, enabling reproducible extraction of ORL instances from OntoNotes. The resulting dataset comprises 97,169 high-quality predicate-argument pairs. Empirical evaluation demonstrates substantial improvements in opinion role identification under low-resource conditions, particularly in cross-task transfer learning and multi-task learning settings. This work establishes a scalable, linguistically grounded data foundation for knowledge transfer across opinion analysis tasks.
📝 Abstract
This report presents a detailed methodology for constructing a high-quality Semantic Role Labeling (SRL) dataset from the Wall Street Journal (WSJ) portion of the OntoNotes 5.0 corpus and adapting it for Opinion Role Labeling (ORL) tasks. Leveraging the PropBank annotation framework, we implement a reproducible extraction pipeline that aligns predicate-argument structures with surface text, converts syntactic tree pointers to coherent spans, and applies rigorous cleaning to ensure semantic fidelity. The resulting dataset comprises 97,169 predicate-argument instances with clearly defined Agent (ARG0), Predicate (REL), and Patient (ARG1) roles, mapped to ORL's Holder, Expression, and Target schema. We provide a detailed account of our extraction algorithms, discontinuous argument handling, annotation corrections, and statistical analysis of the resulting dataset. This work offers a reusable resource for researchers aiming to leverage SRL for enhancing ORL, especially in low-resource opinion mining scenarios.