用大模型自动提取文学文本中的隐喻类类比，省去人工标注。
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
- 基于专家构建新数据集，定义隐喻类比的结构化提取任务。
- 大模型在类比元素生成上表现良好，准确率接近人工水平。
- 适合对文本语义理解与生成感兴趣的学者和开发者。
从自由文本中提取隐喻和类比需要高水平的抽象与语言理解能力。本研究聚焦于从文学文本中提取构成隐喻类比的概念。为此，我们借助领域专家构建了一个新数据集。对比了近期大语言模型（LLMs）在从包含比例类比的文本片段中构建隐喻映射的零样本能力。模型还进一步评估了生成类比中隐含元素的表现，这些元素在文本中未直接出现，需由人类读者推断。实验结果显示，大模型在该任务上取得具有竞争力的结果，令人鼓舞，为自动从文本中提取类比和隐喻开辟了新路径，无需依赖领域专家手动标注数据。
原文摘要 · Abstract (English)
Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain experts. We compare the out-of-the-box ability of recent large language models (LLMs) to structure metaphoric mappings from fragments of texts containing proportional analogies. The models are further evaluated on the generation of implicit elements of the analogy, which are indirectly suggested in the texts and inferred by human readers. The competitive results obtained by LLMs in our experiments are encouraging and open up new avenues such as automatically extracting analogies and metaphors from text instead of investing resources in domain experts to manually label data.
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