用语义元语言构建可验证的情绪分析系统
Faithful by Definition: Emotion Analysis via Natural Semantic Metalanguage Explications
- 将文本转为12类槽位的语义脚本，基于定义规则决策
- 在测试集上达0.33准确率，0.48选择性准确率
- 适合需要可解释情绪分析的研究者与应用
情感分类器的解释通常事后生成，无法保证反映实际计算过程。本文提出一种基于事件的情绪分析显化接口：解析器将输入文本映射为自然语义元语言（NSM）构成的短脚本，包含12个类型化槽位；固定决策规则列表由已发表的语义定义转化而来，仅依赖显化结果计算标签。因此，解释的忠实性具有因果与定义上的保障，所有经验风险仅存在于可审计的解析器中，且通过逐行蕴含接口可追溯至原始输入。在众包事件描述数据集上，微调后的解析器在小规模保留集上达到0.33准确率和0.48选择性准确率，表明该接口以极小精度损失换取了可验证、可检查的决策依据，适用于第一人称事件驱动的情绪分析。同时发布EmoExpl-1200数据集，含逐行验证元数据及完整规则集。
原文摘要 · Abstract (English)
Explanations for emotion classifiers are usually produced post hoc, with no guarantee that they reflect the computation behind the label. We present an explication interface for event-based emotion analysis. A parser maps the input text to an explication, a short script in the closed vocabulary of Natural Semantic Metalanguage organized into twelve typed slots, and a fixed decision list of rules transcribed from published semantic definitions computes the label from the explication alone. The faithfulness guarantee is therefore causal and definitional, while all empirical risk lives in the learned parser, which the per-line entailment interface makes auditable against the input. On crowd-sourced event descriptions, our fine-tuned parser reaches 0.33 accuracy and 0.48 selective accuracy on a small held-out set, suggesting that the interface trades insignificant accuracy difference to a black-box model for a verifiable, inspectable decision basis for first-person event-based emotion analysis. We also release EmoExpl-1200 with per-line verification metadata and the full rule set.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。