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From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ...
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Abstract:
We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation. A key contribution is the combination of semi-automatic resource building for extraction of domain-dependent concern types (with 2-4 hours of human labor per domain) and an entirely automatic procedure for extraction of domain-independent moral dimensions and endorsement values. Prudent (automatic) selection of terms from propositional structures for lexical expansion (via semantic similarity) produces new moral dimension lexicons at three levels of granularity beyond a strong baseline lexicon. We develop a ground truth (GT) based on expert annotators and compare our concern detection output to GT, to yield 231% improvement in recall over baseline, with only a 10% loss in precision. F1 yields 66% improvement over ... : Accepted to Findings of the Association for Computational Linguistics, 2022 ...
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Keyword:
68T50; Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; I.2.7
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URL: https://arxiv.org/abs/2203.10659
https://dx.doi.org/10.48550/arxiv.2203.10659
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