Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
This work addresses the challenge of automating high-level reasoning tasks in natural language processing for literary analysis, though it is incremental as it builds on existing LLM capabilities.
The study tackled the problem of extracting metaphoric analogies from literary texts by constructing a novel dataset with domain experts and evaluating large language models (LLMs) on structuring mappings and generating implicit elements, achieving competitive results that suggest potential for automating analogy extraction.
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.