Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation

TL;DR

This paper introduces a new dataset and evaluates large language models on extracting metaphoric analogies from literary texts, highlighting potential for automated metaphor and analogy extraction.

Contribution

It presents a novel dataset for metaphoric analogy extraction and assesses LLMs' ability to structure and infer implicit analogy elements in literary texts.

Findings

LLMs show promising performance in structuring metaphoric mappings.

Models can infer implicit analogy elements suggested indirectly in texts.

The dataset enables future research in automated metaphor and analogy extraction.

Abstract

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.