| Challenge: | Prior work on metonymy resolution has focused on named entities, but common nouns are also a frequent problem. |
| Approach: | They propose a dataset that combines a metonymy dataset and a chain-of-thought based prompting method for detecting metonyms using large language models. |
| Outcome: | The proposed method can detect metonymy using large language models while still struggling with nuanced semantic understanding. |
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| Challenge: | a recent study has shown that metonymy is a productive and systematic process . linguistic and psycholinguistic studies support the idea that metnomic interpretations are based on lexical ambiguity . |
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Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation (L18-1)
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| Challenge: | Word Sense Disambiguation is a crucial task in Natural Language Processing . supervised systems need to be trained on word-by-word basis, a problem that is beyond reach for resource-rich languages like English. |
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Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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Target Word Masking for Location Metonymy Resolution (2020.coling-main)
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| Challenge: | Existing word sense disambiguation and named entity recognition systems have no explicit metonymy detection. |
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MetFuse: Figurative Fusion between Metonymy and Metaphor (2026.acl-long)
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Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
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Patterns of Polysemy and Homonymy in Contextualised Language Models (2021.findings-emnlp)
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MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (L18-1)
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Exploring Layer-wise Representations of English and Chinese Homonymy in Pre-trained Language Models (2025.findings-acl)
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| Challenge: | lexical ambiguity can arise due to the misunderstanding of its multiple senses. |
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