C on M e C : A Dataset for Metonymy Resolution with Common Nouns (2025)

Abstract: Metonymy plays an important role in our daily communication. People naturally think about things using their most salient properties or commonly related concepts. For example, by saying “The bus decided to skip our stop today,” we actually mean that the bus driver made the decision, not the bus. Prior work on metonymy resolution has mainly focused on named entities. However, metonymy involving common nouns (such as desk, baby, and school) is also a frequent and challenging phenomenon. We argue that NLP systems should be capable of identifying the metonymic use of common nouns in context. We create a new metonymy dataset ConMeC, which consists of 6,000 sentences, where each sentence is paired with a target common noun and annotated by humans to indicate whether that common noun is used metonymically or not in that context. We also introduce a chain-of-thought based prompting method for detecting metonymy using large language models (LLMs). We evaluate our LLM-based pipeline, as well as a supervised BERT model on our dataset and three other metonymy datasets. Our experimental results demonstrate that LLMs could achieve performance comparable to the supervised BERT model on well-defined metonymy categories, while still struggling with instances requiring nuanced semantic understanding. Our dataset is publicly available at: https://github.com/SaptGhosh/ConMeC.

## introduction metonymy is often described as one type of figure of speech that is everywhere in our language. as pointed out by @xcite , metonymy, like metaphor, is part of our everyday way of thinking and is grounded in our experience @xcite . people use metonymy when they refer to one concept or word with something else in a close semantic relation. for example, consider the following sentences: in sentence (a), "desk" refers to people working at the front desk. in sentence (b), "magazine" refers to magazine writer. in (c), "dish" represents the meal being prepared. people don't eat the "dish" itself, but rather the meal served in it. previous work has demonstrated that understanding metonymy is crucial for many nlp tasks, such as information extraction @xcite and named entity recognition @xcite . metonymy resolution is often formatted as a classification task: given one sentence and a target word in it, the system should identify whether the target word is used in a metonymic way. existing datasets for metonymy resolution focus on named entity target word, especially location names, such as semeval 2007 shared task 8 @xcite , relocar @xcite , and wimcor (alex mathews and strube, 2020); or are small and composed of handcrafted simple sentences @xcite . these limitations pose challenges for developing robust machine learning models that can handle a wide range of metonymy types and their diverse manifestations. the narrow focus also makes it difficult to evaluate models' true capabilities in resolving metonymy in natural language. to address this issue, we introduce a new dataset that focuses on metonymy resolution with a common noun target. our dataset consists of 6,000 sentences from wikipedia, where each sentence is paired with a target common noun and annotated by human annotators to indicate whether that common noun is used metonymically or not. it is not an easy task to extract diverse metonymic sentences so we propose an approach to use large language models (llms) with data augmentation to retrieve potential metonymic sentences from a large corpus. inspired by recent advances in larger language models, we introduce a chain-of-thought based prompting pipeline to detect metonymy. specifically, we prompt the model to identify the target word's semantic category and then apply the corresponding chain-of-thought prompt. we apply a self-consistency with the majority vote strategy to improve our model's performance. we evaluate the proposed llm-based method and a supervised transformer-based model on multiple metonymic datasets (including ours). the experimental results show that llms can achieve comparable results to fine-tuned transformer models when the target word belongs to specific semantic categories such as container, location and product. it is still challenging for llms to grasp the nuanced semantic subtleties. we also show that when the transformerbased model is fine-tuned in a cross-category setup, it experiences a significant performance drop, performing worse than llm-based methods. in summary, our contributions are three-fold: ## related work metonymy has long been recognized as a significant concept across multiple disciplines, including philosophy, linguistics, and psychology @xcite . unlike metaphor, metonymy is often approached as a cognitive and pragmatic phenomenon, rather than purely linguistic terms @xcite . for example, @xcite describes metonymy as a conceptual phenomenon and cognitive process. @xcite defines metonymic use as in-troducing a new name -like a nickname -for the entity the speaker has in mind. early in the field of artificial intelligence and natural language processing (nlp), multiple attempts have been made to address the ambiguity issues caused by metonymy, such as collative semantics @xcite and local pragmatics @xcite . more recent work in the nlp community follows @xcite that treats the metonymy resolution as a classification task and focuses on metonymies triggered by named entities. semeval 2007 shared task 8 @xcite ) created a metonymy dataset with sentences extracted from british national corpus (bnc) using keywords of country and company names. later, more work has been done in constructing metonymic datasets, including relocar @xcite and wim-cor (alex mathews and strube, 2020), both of which focuses primarily on named entities referring to geographical locations or organizations. a number of methods have been proposed to tackle the task, including using syntactic roles and morphological features @xcite , external knowledge @xcite , word embeddings @xcite , as well as transformer-based model @xcite . our work is most closely related to @xcite . they created a dataset of 509 artificially constructed metonymic and literal sentence pairs, and proposed a method to determine metonymy by using the contextualized word embeddings. they computed the cosine similarity between the metonymic word and actual paraphrased meaning. for example, in sentence "the man sips the glass", they replace "glass" with "wine", and assume that "glass" should be more semantically similar to "wine" in this sentence than a literal sentence like "the man grabbed the glass". this strategy requires a corresponding literal sentence for each metonymic instance and does not generalize effectively to complex real-world text. in comparison, our dataset builds upon their work by first extracting verbs and nouns from their dataset, using llms to augment these pairs, and then using the expanded set as keywords to extract naturally occurring sentences from wikipedia, which encompasses more complicated and nuanced instances of metonymy. we then used a chain-of-thought prompting technique @xcite to build a 2-step pipeline for identifying metonymy in these sentences. ## dataset creation in this work, we created a new metonymic resolution dataset, conmec (common nouns metonymy corpus), with gold standard human annotations (metonymic vs. non-metonymic) for 6,000 sentences extracted from wikipedia. ## methodology in this work, we focused on investigating how stateof-the-art large language models perform on the task of metonymy resolution. we explored several approaches to prompt the large language model. we first present a method based on the observa- ## experimental results we evaluate our llm-based models on our gold labeled data, as well as three other existing datasets for metonymy resolution. we also present results for the fine-tuned bert as a comparison. for the evaluation metric, we report the precision, recall and f1-score for both metonymic and nonmetonymic examples, and an overall accuracy and macro-f1 score. ## analysis we conduct further analysis to better understand the behavior of the models on our dataset. ## conclusion we introduced a new dataset, conmec, containing 6,000 sentences with human labels for the task of common noun metonymy resolution. we proposed a chain-of-thought inspired prompting method with the state-of-the-art large language models to detect metonymy. we evaluated large language models and a fine-tuned bert model our dataset, as well as three other metonymy datasets. our experimental results demonstrate that with careful design, large language models can achieve comparable results as supervised bert model on particular metonymy categories, although there is still room for improvement that we hope will inspire future work on this task.