用类比关系提升语义角色标注，不依赖标签也能高效准确预测。
FrameNet Semantic Role Classification by Analogy
- 将语义角色建模为框架词元与语义角色对之间的类比关系。
- 在无角色标签训练下实现超越当前最佳的准确率，参数少且收敛快。
- 适合需要轻量级、高效率语义分析的自然语言处理应用。
本文从关系视角出发，将语义角色分类问题建模为 FrameNet 中框架触发词元（LUs）与语义角色对（FEs）之间的类比关系。我们构建了一个新数据集，其中每个二元关系实例被标记为有效（语义角色一致）或无效。该形式化将语义角色分类转化为二元分类任务，训练一个轻量级人工神经网络（ANN），其参数极少且收敛迅速。训练时不引入任何语义角色信息，推理时通过随机采样和类比迁移，在给定框架内计算所有候选语义角色的概率分布以恢复角色。该方法在保持计算高效的同时，超越了以往最优结果。
原文摘要 · Abstract (English)
In this paper, we adopt a relational view of analogies applied to Semantic Role Classification in FrameNet. We define analogies as formal relations over the Cartesian product of frame evoking lexical units (LUs) and frame element (FEs) pairs, which we use to construct a new dataset. Each element of this binary relation is labelled as a valid analogical instance if the frame elements share the same semantic role, or as invalid otherwise. This formulation allows us to transform Semantic Role Classification into binary classification and train a lightweight Artificial Neural Network (ANN) that exhibits rapid convergence with minimal parameters. Unconventionally, no Semantic Role information is introduced to the neural network during training. We recover semantic roles during inference by computing probability distributions over candidates of all semantic roles within a given frame through random sampling and analogical transfer. This approach allows us to surpass previous state-of-the-art results while maintaining computational efficiency and frugality.
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