Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929642024206336 |
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| author | Boisson, Joanne Siddique, Zara Borkakoty, Hsuvas Antypas, Dimosthenis Anke, Luis Espinosa Camacho-Collados, Jose |
| author_facet | Boisson, Joanne Siddique, Zara Borkakoty, Hsuvas Antypas, Dimosthenis Anke, Luis Espinosa Camacho-Collados, Jose |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15375 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation Boisson, Joanne Siddique, Zara Borkakoty, Hsuvas Antypas, Dimosthenis Anke, Luis Espinosa Camacho-Collados, Jose Computation and Language 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. |
| title | Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.15375 |