用大模型自动选词义，无需人工标注数据。
Integrating Symbolic Natural Language Understanding and Language Models for Word Sense Disambiguation
- 用符号系统生成候选词义，转为自然语言提问LLM
- 在无标注数据下达到接近人类水平的准确率
- 适合需要精细词义推理的智能系统开发
词义消歧是自然语言理解的核心挑战。现有方法主要针对粗粒度表示（如WordNet同义词集或FrameNet框架），且需人工标注训练数据构建，难以自动处理更丰富的表示（如基于OpenCyc的结构）。本文提出一种新方法：以统计语言模型作为消歧代理，无需任何人工标注。符号化自然语言理解系统生成多个候选词义，转化为可区分的自然语言表述，输入大语言模型以根据上下文选择合适解释，并将选定词义回传至符号系统。在与人工标注黄金标准对比的评估中，该方法展现出有效性。
原文摘要 · Abstract (English)
Word sense disambiguation is a fundamental challenge in natural language understanding. Current methods are primarily aimed at coarse-grained representations (e.g. WordNet synsets or FrameNet frames) and require hand-annotated training data to construct. This makes it difficult to automatically disambiguate richer representations (e.g. built on OpenCyc) that are needed for sophisticated inference. We propose a method that uses statistical language models as oracles for disambiguation that does not require any hand-annotation of training data. Instead, the multiple candidate meanings generated by a symbolic NLU system are converted into distinguishable natural language alternatives, which are used to query an LLM to select appropriate interpretations given the linguistic context. The selected meanings are propagated back to the symbolic NLU system. We evaluate our method against human-annotated gold answers to demonstrate its effectiveness.
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