Venue: NAACL 2025

# ConMeC: A Dataset for Metonymy Resolution with Common Nouns

Saptarshi Ghosh and Tianyu Jiang

University of Cincinnati

ghosh2si@mail.uc.edu, tianyu.jiang@uc.edu

###### 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,1 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.

Footnote 1: [https://github.com/SaptGhosh/ConMeC](https://github.com/SaptGhosh/ConMeC)

## 1 Introduction

Metonymy is often described as one type of figure of speech that is everywhere in our language. As pointed out by Lakoff and Johnson (1980), metonymy, like metaphor, is part of our everyday way of thinking and is grounded in our experience [7]. 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:

_(a) The **desk** called the school police when the babies got into a fight._

_(b) The **magazine** wrote, "She turned thousands of teens into readers."_

_(c) This **dish** may be prepared with salmon, stingray and even puffer fish._

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 [35] and named entity recognition [41]. 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 [26], RelocaR [42], and WIMCOR [1]; or are small and composed of handcrafted simple sentences [34]. 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 transformer-based 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:

1. We introduce a dataset of 6,000 sentences with human annotation indicating whether the target word is metonymic. The dataset is freely available at: [https://github.com/SaptGhosh/ConMeC](https://github.com/SaptGhosh/ConMeC).
2. We propose using large language models with a chain-of-thought based prompting method and a self-consistency strategy for this task.
3. We conduct extensive experiments on our dataset, as well as three other metonymy datasets. Our analysis provides insightful findings for future research on the task.

## 2 Related Work

Metonymy has long been recognized as a significant concept across multiple disciplines, including philosophy, linguistics, and psychology Noppen (1985). Unlike metaphor, metonymy is often approached as a cognitive and pragmatic phenomenon, rather than purely linguistic terms Jodlowiec and Piskorska (2015). For example, Radden and Koveces (1999) describes metonymy as a conceptual phenomenon and cognitive process. Papafragou (1996) defines metonymic use as introducing 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 Fass (1991) and local pragmatics Hobbs and Martin (1987). More recent work in the NLP community follows Markert and Nissim (2002) that treats the metonymy resolution as a classification task and focuses on metonymies triggered by named entities. SemEval 2007 Shared Task 8 Markert and Nissim (2007) 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 Gritta et al. (2017) and wincorAlex 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 Nicolae et al. (2007), external knowledge Nastase and Strube (2009), word embeddings Grittta et al. (2017), as well as transformer-based model Li et al. (2020).

Our work is most closely related to Pedinotti and Lenci (2020). 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 Wei et al. (2022) 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 N**ouns **Met**onymy Corpus), with gold standard human annotations (Metonymic vs. Non-Metonymic) for 6,000 sentences extracted from Wikipedia. Table 1 shows some examples of our gold dataset. As far as we are concerned, it is the largest metonymic resolution dataset that focuses on common nouns compared to previous work on named entity metonymy. We believe that the dataset would not only be used as a benchmark for intrinsic evaluation on metonymic resolution, but also serve as a valuable resource to assess NLP pipelines' ability to understand implicit languages in a human-like manner.

### Metonymy Types

We followed Pedinotti and Lenci (2020) to focus on the six most common types of metonymy: container-for-content, producer-for-product, product-for-producer, location-for-located, causer-for-result, and possessed-for-possessor. For example, _"the man sips the glass"_ belongs to the container-for-content category because "glass" actually refers to the drink contained in the glass. In the sentence "_the singer told the magazine_", the "magazine" refers to the producer of the magazine, i.e., the journalist, so it is categorized as product-for-predducer. For simplicity, in the rest of the paper, we will use the first word of the type name interchangeably (such as container).

### Data Augmentation Using LLM

It is not an easy task to build a dataset of non-artificial metonymic sentences due to their relative scarcity in typical discourse. To begin with, we collected the common nouns and the associated activity verbs from the handcrafted metonymic sentences in Pedinotti and Lenci (2020), such as \(<\)glass, \(\text{sip}\)\(>\) and \(<\)magazine, write\(>\). Though they do not always guarantee metonymy, we use them as effective seeds for identifying potential metonymic instances with a higher likelihood. Their dataset contains 509 artificial examples of metonymy with 221 common noun entities associated with 354 verbs, creating 368 unique noun-verb pairs. We can then collect the sentences containing these \(<\)noun, verb\(>\) pairs from a large corpus. However, one limitation of this extraction method is the lack of expression diversity. To include more diverse metonymic usage, we applied a data augmentation technique to increase noun-verb combinations for extraction.

Our goal is to take advantage of large language model's generalization ability to learn more metonymic events. Specifically, we used Llama-3.1-8B Dubey et al. (2024) to generate three alternate words in place of the noun in the existing metonymic sentence from the Pedinotti and Lenci (2020) dataset. We then fed the new sentences into Llama to generate three alternative verbs. For example, suppose the original metonymic sentence is "_the man sips the glass_". We would first learn replacement nouns such as _mug, cup_ and _tankard_. Then, by asking Llama to replace the verb in the new sentences with augmented nouns, we can retrieve more activity verbs associated with the nouns, such as _taste, drink_ and _gulp_. The augmentation pipeline generates a total of 611 nouns, 1,109 verbs, and 4,397 unique noun-verb pairs.

We then used the spaCy Honnibal et al. (2020) dependency parser to extract all sentences with these noun and verb pairs from Wikipedia (dump as of 2022-03-01). We required that there exists a dependency relation between the noun and verb in the sentence. This generated a total of 108,408 sentences, with 418 unique nouns, 564 unique verbs, and 2,240 unique \(<\)noun, verb\(>\) pairs over six metonymy types. For the annotation purpose, we randomly sampled 1,000 sentences from each metonymic category (6,000 sentences in total). To avoid the dominance of some commonly used nouns such as "_dish_" and "_city_", our sampling method adopted a uniform distribution across different nouns. Our data collection pipeline is shown in Figure 1.

Figure 1: Process of dataset creation.

 



### Human Annotation

We recruited two human annotators to complete the annotation task. To prepare the annotators, we presented them with the definitions of metonymy and a few examples for each category. Each time, annotators were given one sentence and one target word (noun) in the sentence, and they were asked to determine whether the target word was used metonymically or not. When the annotations were finished, we measured the inter-annotator agreement using Cohen's kappa. Different metonymy types received variant kappa scores, with the highest being location-for-located (0.94) and lowest being possessed-for-possessor (0.75). The overall kappa score is 0.83.

To create the final set of gold standard labels, we had the annotators adjudicate their disagreements. Table 2 shows the number of metonymic sentences for each type in our final dataset. Each category consists of 1,000 sentences, with location having the most (454) and producer having the least (129) number of metonymic sentences. In general, around 28% of the sentences were annotated as Entonymic. Table 1 shows some annotated examples in different types.

## 4 Methodology

In this work, we focused on investigating how state-of-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 observation that metonymic sentences typically trigger a semantic category change in the target word. We then introduce a 2-step prompting approach, where the model is first asked to categorize the target noun and then detect any semantic category change. We further show that our model's performance can improve through a self-consistency with majority vote approach. Finally, we fine-tune a BERT model on our dataset to compare with large language models approach.

### Chain-of-Thought Prompting

We initially explored prompting large language models with the definition of metonymy and a few examples Brown et al. (2020), then asking the model to classify new instances. However, the model struggled with a consistent understanding of the metonymy phenomenon. Inspired by the success of chain-of-thought Wei et al. (2022) in many NLP tasks, we propose a chain-of-thought based

\begin{table}
\begin{tabular}{l l|c} \hline \hline  & Metonymy & Non-Metonymy \\ \hline container & 226 & 774 \\ producer & 129 & 871 \\ product & 406 & 594 \\ location & 454 & 546 \\ causer & 317 & 683 \\ possessesed & 183 & 817 \\ \hline Total & 1,715 & 4,285 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of metonymic and non-metonymic examples across six categories. Each category contains 1,000 sentences (6,000 annotated sentences in total).



prompting method.

Since the most familiar definition of metonymy is "using one entity to refer to another that is related to it" Lakoff and Johnson (1980), an intuitive idea is to use chain-of-thought to prompt the large language models to identify if there exists a _reference shift_, in which a linguistic sign refers not only to its default concept, but to another concept Adel (2014). However, a direct instruction about a reference shift will cause the LLMs to generate excessive false positives. For example, given the sentence "_she browses a **newspaper**_, the model will predict yes and explain that it contains a reference shift because the newspaper refers to the content of the newspaper.

The idea of our method is based on the observation that reference shift often co-occurs with a change of semantic categories, which serves as a key indicator for identifying metonymic expressions. This observation is aligned with previous work on the cognitive bases of metonymy describing it as "a specific semantic relation" Koch (1999). For example, in the sentence "_the city celebrated the festival_", the target word (noun) "city" refers to the people in the city, not city as a location. Similar situation exists for the "church" in _"the man devotedly followed the church"_. The shift from one semantic category (location) to another (people/organization) provides a clue for detecting metonymy.

Building on this insight, we designed a prompt to capture these transitions between semantic categories. The prompt guides the model to first identify any shift between semantic categories in the context of the target word through a series of reasoning steps, and then determine if this categorical transition indicates a metonymic usage.

### 2-Step Pipeline with Target Word Categorization

Through semantic based chain-of-thought prompting, we observed that the model was able to identify metonymy more efficiently. However, the model would sometimes miss the nuanced and subtle cases of metonymy due to the complexity of natural language. One common error is caused by word sense disambiguation. For example, in the sentence "_the dish had to be pointed directly at the satellite, with nothing blocking the signal_", "dish" refers to a satellite dish, not the common dinnerware, and there is no metonymy implied in this instance. However, our model made a wrong prediction by identifying a semantic category change of the word "dish".

To overcome this challenge, we design a 2-step pipeline. We first provide the target word to the

Figure 2: The architecture of our 2-step prompting method. We illustrates two examples. The LLM will first determine the semantic category of the target word in the sentence, such as container or location. Then, given the category dependent prompting, the model should predict whether there exists a metonymy use or not.

 

LLM and ask it to predict a category from five options: container, producer, product, location, and general. We do not include causer or possessed, as the target words in these categories do not refer to a particular type of semantic category like the others. Based on the selection of the model, we apply a semantic category dependent chain-of-thought prompting at the second stage. We create five different chain-of-thought prompts for each category, instead of one universal chain-of-thought prompt. This way, the model is able to identify the challenging and implicit cases of metonymy more efficiently. Figure 2 shows the architecture of our pipeline with an example of two sentences in different categories.

### Self-Consistency with Majority Vote

Previous studies have demonstrated that large language models often suffer from semantic consistency issue, i.e., they can sometimes generate contradictory outputs when presented with prompts with similar or equivalent semantics Pezeshkpour and Hruschka (2024); Yang et al. (2024). The idea of majority vote and its variants have achieved a good success in improving LLMs performance, such as the self-consistency strategy Wang et al. (2023), multi-agents Du et al. (2024), and self-agreement Lin et al. (2024). We observed similar inconsistency in our model's output. Following the same idea, we adopted a majority vote method from multiple runs.

## 5 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 non-metonymic examples, and an overall accuracy and macro-F1 score.

### LLMs and Gold Supervision

Table 3 shows our experimental results. The table describes four primary sections: variations of Llama-3.1-8B and Llama-3.1-70B, GPT-4o and the supervised BERT. The first and second sections describe four different setups of Llama-3.1-8B and Llama-3.1-70B respectively: 1) **Basic**: Simple prompt asking the model if the sentence is metonymic or not; 2) **CoT**: One general chain-of-thought prompt with semantic category change; 3) **CoT-2S**: Our 2-step pipeline - first identify the semantic category of the target word, and then apply the corresponding chain-of-thought prompt; 4) **CoT-2S-SC**: We run the Llama model multiple times and use the majority vote to determine the final result. We use the temperature 0.4 for the categorization step, and 0.6 for the chain-of-thought step, with top-p 0.9. As can be seen from the table, Llama 70B consistently outperforms Llama 8B. When using the smaller 8B model, the Basic prompt has better performance than CoT. But

\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Metonymic**} & \multicolumn{3}{c}{**Non-Metonymic**} \\ \cline{3-10}
**Model** & **Acc** & **Macro-F1** & Pre & Rec & F1 & Pre & Rec & F1 \\ \hline Llama-8B-Basic & 61.2 & 61.2 & 39.0 & 60.2 & 47.3 & 79.0 & 71.4 & 75.0 \\ Llama-8B-CoT & 44.3 & 44.0 & 33.3 & **91.6** & 48.9 & 87.9 & 25.1 & 39.1 \\ Llama-8B-CoT-2S & 65.2 & 61.0 & 42.8 & 50.8 & 46.5 & 78.7 & 72.8 & 75.6 \\ Llama-8B-CoT-2S-SC & 66.5 & 62.0 & 44.3 & 52.8 & 48.1 & 77.6 & 73.6 & 75.9 \\ \hline Llama-70B-Basic & 71.5 & 66.5 & 50.7 & 58.2 & 54.2 & 81.8 & 76.3 & 78.9 \\ Llama-70B-CoT & 68.8 & 66.6 & 47.6 & 75.2 & 58.3 & 86.7 & 66.1 & 75.0 \\ Llama-70B-CoT-2S & 78.1 & 73.6 & 57.5 & 70.6 & 63.3 & 87.9 & 80.2 & 83.9 \\ Llama-70B-CoT-2S-SC & 79.4 & 76.5 & 61.9 & 76.3 & 68.3 & 89.1 & 80.6 & 84.6 \\ \hline GPT-4o & 82.6 & 77.9 & 75.6 & 59.7 & 66.7 & 85.2 & **92.3** & 88.6 \\ \hline BERT & **86.6** & **82.9** & **77.4** & 74.2 & **75.2** & **89.8** & 91.3 & **90.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for all models. **Llama-Basic**: simple prompt asking Llama whether a sentence is metonymic or not. **Llama-CoT**: one general chain-of-thought (CoT) prompt for all categories. **Llama-CoT-2S**: 2-step (2S) chain-of-thought prompt. **Llama-CoT-2S-SC**: 2-step chain-of-thought prompt with self-consistency (SC) method using the majority vote across multiple runs. **GPT-4o**: 2-step chain-of-thought (CoT-2S) using GPT-4o’s API. **BERT**: supervised BERT with 5-fold cross validation.

 

we get opposite results on the 70B. By adding the 2-step pipeline, Llama-70B-CoT-2S receives 7.0 points higher macro-F1 score than 70B-CoT while Llama-8B barely improves. We believe this is due to the size of the 70B model. Llama-8B and particularly 70B further benefits from the majority vote method.

In the third section, we show the performance of GPT-4o (gpt-4o-2024-05-13) using our 2-step prompting (same as **CoT-2S**). GPT-4o achieves better performance than Llama, outperforming the best Llama model using majority vote (Llama-70B-CoT-2S-SC) in a single run. Due to budget limitations, we didn't run the self-consistency method with GPT-4o. The breakdown of precision and recall reveals that GPT-4o is more conservative to predict metonymy, leading to higher precision but lower recall for the true metonymy instances compared to Llama.

The final section shows the performance of supervised method. We fine-tuned BERT-base-uncased Devlin et al. (2019) with 5-fold cross validation on our dataset. We used random seeds to run the experiments 5 times and report their average score. We used the last hidden state of the [cls] token and appended a linear layer for binary classification. The learning rate was set at 1e-5 and Adam optimizer was used. Training was conducted for 3 epochs with a batch size of 16. It is not a surprise that supervised BERT has the best performance among all the models, with an overall accuracy of 86.6 and macro-F1 of 82.9.

### Evaluation on Other Metonymy Datasets

We also evaluated the 2-step pipeline model (Llama-70B-CoT-2S) on a few existing metonymy dataset. ReLocaR Gritta et al. (2017) and SemEval 2007 Shared Task 8 Markert and Nissim (2007) are two datasets focusing solely on named entity metonymy, specifically geographical location and organization named entities. Due to this, we modified our 2-step model for these datasets by removing the categorization from the first step, only using the chain-of-thought prompt for the location category, saving significant computational time. ReLocaR has 496 metonymic and 486 non-metonymic examples also extracted from Wikipedia. SemEval contains 167 metonymic and 721 non-metonymic instances from British National Corpus. Pedinotti and Lenci (2020) contains 509 metonymic and 509 non-metonymic hand-crafted sentences.

For a comparison, we evaluated the BERT model on each dataset as well. We fine-tuned BERT on each dataset using the same learning rate and optimizer as the previous experiment. For ReLocaR and SemEval2007, we used the original training set given in these datasets to train BERT and reported the result on their test set. For the Pedinotti and Lenci (2020) and our dataset, we ran the 5-fold cross validation with random sampling.

Table 4 shows the F1-score for both metonymic and non-metonymic instances. BERT has superior F1 scores for both metonymic and non-metonymic across all datasets. However, on the ReLocaR dataset, we can see that Llama significantly narrows the gap with the supervised method. Both BERT and Llama receive a lower score on our dataset compared to Pedinotti-Lenci. This is as expected since our metonymic sentences are longer and semantically more complex than artificial sentences such as "_the man sips the glass_", making it a more challenging task.

### Performance Breakdown Across All Categories

Figure 3 shows the performance comparison of Llama, GPT-4o and BERT across the six metonymic categories. The Llama scores are re

\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Llama**} & \multicolumn{2}{c}{**BERT**} \\ \cline{2-5}
**Dataset** & Met & Non & Met & Non \\ \hline Our dataset (ConMeC) & 63.3 & 83.9 & 75.2 & 90.6 \\ Pedinotti-Lenci & 72.7 & 73.6 & 81.9 & 83.4 \\ ReLocaR & 86.1 & 82.9 & 90.2 & 90.0 \\ SemEval2007-Task8 & 50.0 & 72.8 & 61.4 & 90.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1-score comparison of Llama-70B-CoT-2S and BERT on metonymic and non-metonymic instances across different datasets.

Figure 3: F1-scores across six categories among Llama (blue), GPT-4o (green) and BERT (yellow) on the metonymic sentences.

 

ported on the best Llama model (70B-CoT-2S-SC). We can see from the chart that both Llama and GPT-4o achieved comparable results with BERT on the first four categories, with GPT-4o even outperforming BERT on the container category. However, for causer and possessed, LLMs witnessed a big performance drop as compared to BERT. This can be explained by the semantic complexity of these two categories - _causer_ or _possessor_ could be objects, people, or many other semantic types. Our 2-step prompt for these two categories does not specify one particular semantic type. Using a general prompt does not cover the complexities associated with metonymy like the four other categories, leading to a low performance. This leaves room for improvement in future work.

## 6 Analysis

We conduct further analysis to better understand the behavior of the models on our dataset.

### Error Cases

Table 5 outlines the errors made by Llama-70B and GPT-4o using CoT-2S architecture in a single run. In sentence (1), "_a glass of whisky_" should not be considered container-for-content metonymy because the content (_whisky_) is already present in the sentence. Llama still struggles with this type of cases even though our prompt explicitly covers such a scenario. Sentence (2) represents a straightforward example, where Llama made the wrong prediction. From our observation, Llama is more prone to random errors, which can be mitigated by the self-consistency method.

Sentence (3-6) demonstrate that the target word could sometimes be used both metonymically and literally within the same sentence. For instance, "_Yiddish-speaking metropolis_" in sentence (5) is a metonymic phrase referring to the people in the metropolis speaking Yiddish, whereas the broader context uses _metropolis_ as a location. Similarly in sentences (3) and (4), the metonymy is implied through the phrases "_most cited European social scientist_" and "_most widely read Swedish novelist_". We further discuss these instances in Appendix C. In sentence (6), the target word "_stadium_" appears twice in the sentence - once tenonymically and once literally. These scenarios are tricky for models to identify the correct instance of the target word.

Sentence (7) and (8) are examples of causer-for-result metonymy where most subtleties lie. For example, "_Holland first heard the Native American flute at a concert..._", the word _flute_ refers to the music caused by playing the instrument, not the instrument itself. The models struggle with these nuanced implication of metonymy. Sentence (9) and (10) belongs to the possessed category, where Llama in particular struggles, and tend to label sentences as metonymic.

### Cross Category Gold Supervision

In our experiments with BERT in Table 3, we trained the model using 5-fold cross-validation on the entire dataset. The model can train and test on the same metonymic category. To assess BERT's performance in a cross-domain setting, we trained



the model on five categories and tested it on the sixth. Table 6 shows the F1-score for metonymic sentences under this setup. Comparing the cross category with the general supervised method results, the F1-score experiences a significant drop. This illustrates the limitations of the fine-tuned BERT model, namely its reliance on high quality training data of the same category or type. In contrast, LLMs do not have this constraint, and they also demonstrate a better generalization ability. This flexibility is a significant advantage of LLMs, allowing them to generalize across diverse categories.

### Does Context Help?

In all previous experiments, we provided the model only with one sentence and its target word to determine metonymy. To assess whether additional context can help the model make predictions, we provide one preceding and one following sentence to the model. Table 7 shows the results of this context experiment, comparing the performance of our best model (Llama-70B-CoT-2S) and fine-tuned BERT. By adding the context, BERT shows a minor drop in accuracy and F1-score, while the drop in performance for Llama is more noticeable. This indicates that additional context might introduce noise or disrupt LLMs ability to make the correct decision.

### Number of Votes

For the self-consistency method, we ran the experiments multiple times for the CoT-2S model to decide on the majority vote. Table 8 shows the results of running the self-consistency method on the Llama 70B-Basic, 8B-CoT-2S and 70B CoT-2S models. The performance does not significantly improve for the 70B-Basic and 8B-CoT-2S models. But the 70B-CoT-2S model benefits significantly from the voting, gaining a F1-score of 5.0 on metonymic examples. This is because the chain-of-thought reasoning process can explore multiple reasoning paths when determining metonymy in a sentence. However, the model occasionally follows an incorrect path or logic. Voting helps mitigates this issue. The smaller model 8B-CoT-2S does not exhibit similar improvements on majority voting compared to the 70B-CoT-2S. This observation aligns with previous findings that indicate larger model size generally leads to better performance [34, 9].

## 7 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.

\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Cross Category**} & \multicolumn{3}{c}{**General**} \\ \cline{2-7}
**Category** & Pre & Rec & F1 & Pre & Rec & F1 \\ \hline container & 86.5 & 17.4 & 28.3 & 85.7 & 53.1 & 65.5 \\ producer & 21.7 & 43.7 & 29.6 & 86.1 & 43.4 & 57.7 \\ product & 64.5 & 72.7 & 68.2 & 89.9 & 79.3 & 84.2 \\ location & 84.1 & 47.6 & 60.5 & 89.0 & 75.7 & 81.6 \\ causer & 51.2 & 32.6 & 40.6 & 78.7 & 69.0 & 73.6 \\ possessed & 47.4 & 33.5 & 39.7 & 82.0 & 65.0 & 72.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: BERT performance on our dataset. Cross Category: train on 5 categories and test on the rest one category. General: 5-fold cross validation on the whole annotated dataset.

\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Llama**} & \multicolumn{3}{c}{**BERT**} \\ \cline{2-7}  & Acc & Met & Non & Acc & Met & Non \\ \hline With Context & 74.5 & 58.1 & 80.3 & 86.2 & 75.0 & 90.5 \\ Without Context & 78.0 & 63.3 & 83.9 & 88.6 & 75.2 & 90.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overall accuracy and F1-scores of metonymic and non-metonymic sentences for two models with and without the context.

 



### Limitations

The use of prompting with large language models requires substantial computational time. In our best model setting (Llama-70B-CoT-2S), it takes around 40 hours to inference on 6,000 examples with 2 NVIDIA A100 GPUs. This time requirement presents challenges for analyzing extensive datasets or extracting metonymic sentences from large-scale corpora.

In this work, we have restricted our investigation to focus on six common categories of metonymy, even though our proposed method can be potentially be adapted to other categories. We would like to extend our work to study a diverse range of metonymic expressions. We will leave this for future work.

We have not yet evaluated the effectiveness of our model on any downstream NLP applications. It remains a curious question as how the integration of our metonymy resolution system could affect the overall performance on existing NLP benchmarks. Metonymy is still a challenging problem for NLP. We hope our work can attract more attention to this problem.

## Acknowledgments

We thank the Cincinnati NLP group for their constructive comments. We also thank the anonymous NAACL reviewers for their valuable suggestions and feedback.

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Prompts for Our Best Model

Table 11 features all the prompts in our models. In CoT model, only the general prompt is used, while all categories are used in CoT-2S model.

For negative examples, we instruct the model to provide a one-word answer in the last step, saying the sentence is "literal", instead of "nonmetonymic", as shown in Figure 2. This ensures that word matching can detect the model's final prediction, even if it does not follow the instruction to give a one-word response in the last step.

## Appendix B DeepSeek Comparison

With the recent launch of DeepSeek R1 in January 2025 (DeepSeek-Al et al., 2025), the model has demonstrated performance on par with GPT-4o across a wide range of tasks, including math solving, code generation, and natural language understanding. Given its promising capabilities, we evaluate its effectiveness on our dataset to compare its performance against Llama and GPT-4o.

Table 9 presents the results of a single-run evaluation of DeepSeek R1 on our dataset, compared with Llama and GPT-4o. For this experiment, we utilized our best performing 2-step chain-of-thought architecture with the DeepSeek 70B distilled model.

The results show that DeepSeek achieves noticeably better performance than Llama, with its F1-score being 3.4 points higher on metonymic examples. Llama models exhibit a high recall for metonymic sentences, as evident from Table 3. DeepSeek's results align more closely with GPT-4o, achieving higher precision but lower recall, indicating that it is more conservative at predicting metonymy. However, it is also worth mentioning that DeepSeek-R1-Distill-Llama-70B model takes around 5x longer time than Llama 70B to make predictions for all 6,000 sentences in our dataset, as shown in Table 10. For all models, we set the same maximum output lengths.

## Appendix C Metonymy in Single Noun vs. Noun Phrase

Our prompts are designed to detect metonymy by identifying the transition on semantic categories associated with a single noun in the sentence. One limitation of this concept is that sometimes the category change occurs in the noun phrases, rather than the single noun itself. This is particularly common in the producer category. In the sentence "_The **author William Shakespeare** is read all over the world_", one can argue that the transition lies predominantly in the noun phrase "_author William Shakespeare_", rather than just the noun "_author_".

Sentence (3) and (4) in Table 5 are examples where the noun is used metonymically within the noun phrase, but literally across the sentence. In sentence (3), the noun _scientist_ in the phrase "_most cited European social scientist_" refers to the work of the scientist being cited. But in a broader context, scientist refers to the person themselves. Similarly, in the phrase "_Yiddish-speaking metropolis_" in sentence (4), _metropolis_ refers to the people of the metropolis in this context, however it also refers to the location across the entire sentence. This highlights the diversity and challenges that our dataset offered, leaving room for future research to address these various linguistic instances.

## Appendix D Target Word Miscategorization

The chain-of-thought prompt used in our CoT-2S model relies on the accuracy of the categorization performed in the first step. While the categorization is generally accurate, errors are more frequent for target words in the possessed and causer category. This is because these categories do not correspond to a well-defined semantic class, making them harder to categorize than the other four. Table 12 presents the outputs of Llama-70B and GPT-4o using our CoT-2S architecture.

\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Llama-8B** & **Llama-70B** & **DeepSeek-R1** \\ \hline Running Time (hrs) & 13 & 40 & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Inference time comparison on our 6,000 sentences with different models: Llama-3.1-8B, Llama-3.1-70B, DeepSeek-R1–Distill-Llama-70B, all using the CoT-2S setting. The 8B model takes one NVIDIA A100 GPU and 70B model takes two NVIDIA A100 GPUs.

\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Metonymic**} & \multicolumn{3}{c}{**Non-Metonymic**} \\ \cline{2-7}
**Model** & Pre & Rec & F1 & Pre & Rec & F1 \\ \hline Llama & 57.5 & **70.6** & 63.3 & **87.9** & 80.2 & 83.9 \\ GPT-4o & **75.6** & 59.7 & **66.7** & 85.2 & **92.3** & **88.6** \\ DeepSeek R1 & 72.6 & 60.4 & 65.7 & 83.7 & 90.2 & 86.8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of three models on our dataset in a single run. **Llama**: 2-step chain-of-thought (CoT-2S) using Llama-70B. **GPT-4o**: CoT-2S using GPT-4o’s API. **DeepSeek R1**: CoT-2S using distilled DeepSeek-R1-Distill-Llama-70B.

 



**Prompt** & **Category**

Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as "bottle" in "liquid in the bottle"). You will be given a sentence and a target word. The category of the target word is a container. Your task is to determine if the target word in the sentence is used in a metonymic sense or literal sense. Think in the following steps:

1) Is the target word still used in the sense of a container? If yes, proceed to the next step. If not, then the sentence is not metonymic. In that case, do not perform the next steps, go to step 7 and say that it is literal.

2) Determine if there is a shift of the semantic category, i.e., whether the true semantic meaning of that word still belongs to that category.

3) If there is a shift, has the semantic meaning of the word shifted from container to content or process?

4) Does the target word refer to another entity?

5) If the change in semantic meaning is from container to content, is the content already present in the sentence? If yes, it is not metonymic. So go to step 7 and say it is literal.

6) Use this comparison to determine if there is a metonymy in the sentence.

7) In the last step, give a one-word answer saying "Final answer: netonymic" or "Final answer: literal".

\begin{table}
\begin{tabular}{|p{190.7pt}|c|} \hline  & **Property** & **Category** \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “bottle” in “liquid in the bottle”). You will be given a sentence and a target word. The category of the target word is a container? &container \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “bottle” in “liquid in the bottle”). You will be given a sentence and a target word. The category of the target word is a container? &container \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “bottle” in “liquid in the bottle”). You will be given a sentence and a target word. The category of the target word is a container? &container \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “bottle” in “valid in the bottle”). You will be given a sentence and a target word. The category of the target word is a product. You need to identify if the target word in the sentence is modeler. This in the following steps:

1) Determine if there is a shift of the semantic category, i.e., whether the true semantic meaning of that word is still a product.

2) If there is a shift, has the semantic meaning of the word shifted from producer to product?

3) Does the word refer to the producer itself? If so, then the sentence is literal.

4) Does the word refer to the product of the product? In that case, the sentence is metonymic.

5) Use this comparison to determine if there is a metonymy in the sentence.

6) In the last step, give a one-word answer saying ”Final answer: netonymic” or “Final answer: literal”.

\begin{tabular}{|p{190.7pt}|c|} \hline  & **PROUCT** \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “magazine” in “editor or author producing the magazine”). You will be given a sentence and a target word. The category of the target word is a product. You need to identify if the target word in the sentence is used in a metonymic sense or literal sense. Think in the following steps:

1) Has the semantic meaning of the word shifted from product to producer or organization?

2) Check if the word refers to the producer of the product in any part of the sentence, instead of the physical product. (Example– the magazine criticized the man”, where “magazine” does not refer to the physical magazine but the producer of the magazine. It is metonymic in such a case).

3) Check if the word refers to the organization that creates the product in any part of the sentence, instead of the physical product. (Example- “She worked for the Sun magazine” where “magazine” refers to the company that produces the magazine and not the physical magazine). It is metonymic in such a case.

4) Determine whether the word is used in a metonymic sense using the above-mentioned steps.

5) In the last step, give a one-word answer saying “Final answer: netonymic” or “Final answer: literal”.

\begin{tabular}{|p{190.7pt}|c|} \hline  & **Location** \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “city” in “residents of the city”). You will be given a sentence and a target word. The category of the target word is a location. You need to identify if the target word in the sentence is used in a metonymic sense. Think in the following steps:

1) Determine if there is a shift of the semantic category, i.e., whether the true semantic meaning of that word has shifted from location.

2) Check if the target word (location) indicates the people of the location (Example - “The city/island/pub hosted the event” where “city/pub/island” indicates the people of that location hosting the event, and not the physical location). If so, then it is metonymic.

3) Check if the target word (location) refers to the activity (Example - “He devotredly followed the church” where “church” represents the beliefs of the church and not the physical church). If so, then it is metonymic.

4) Check if the target word (location) refers to an institution (Example; “the department was notified” where “department” refers to the institution and not the physical location). If so, then it is metonymic.

5) Use this comparison to determine if there is a metonymy in the sentence.

6) In the last step, give a one-word answer saying “Final answer: netonymic” or “Final answer: literal”

\begin{tabular}{|p{190.7pt}|c|} \hline  & **General** \\ \hline Metonymy is a figure of speech that substitutes the name of one thing for that of another of which it is an attribute or with which it is associated (such as “crown” in “the monarchy”). You will be given a sentence and a target word. You need to identify if the target word in the sentence is used in a metonymic sense or literal sense. Think in the following steps:

1) Is there a shift in the semantic meaning of the target word when it is used in the sentence?

2) If the meaning has shifted, does it refer to a different semantic category? If the semantic meaning has shifted to a category, then the word is used in a metonymic sense. Otherwise, the word is used in a literal sense.

3) Does the target word refer to something else of which it is an attribute or?

4) Use the comparison to determine if there is metonymy in the sentence?

5) In the last step, give a one-word answer saying “Final answer: netonymic” or “Final answer: literal” &container \\ \hline \end{tabular}
\end{table}
Table 11: Prompts used in our models. In CoT model, only the general prompt is used, while all categories are used in the CoT-2S model.





\begin{table}
\begin{tabular}{|p{142.3pt}|p{190.7pt}|p{156.8pt}|} \hline
* The word “scientist” refers to a person who produces research and publications. In this sentence, “scientist” refers to the person himself and not the product (publications). Since there is no category shift, we proceed to step 3. Does the word refer to the product of the project – Yes, an object with a predicate type. 4. Does the word refer to the product of the project – No, the word does not refer to the product. 5. Use this comparison to determine if there is a metonymy in the sentence: Since the isotropy/scientist” refers to the producer themselves and not their product, there is no metonymy. 6. Final answer: LITERAL & _Dedicated




**Example Information**

[1cm] **Llama-CoT-2S Output**

[1cm] **GPT-4o Output**

[1cm] **JLocation**

To determine if the target word "metropolis" is used in a metonymic or literal sense, let's follow the steps:

\({}^{1}\) Determine if there is a shift of the semantic category: The word "metropolis" is a location, and it refers to a large city, which is a type of location. There is no apparent shift in the semantic category.

2. Check if the target word indicates the PEOPLE of the location: In this sentence, "metropolis" does not indicate the people of the location. If describes the location itself as a large, Yiddish-speaking city.

3. Check if the target word refers to the activity: "metropolis" does not refer to any activity in this sentence.

4. Check if the target word refers to an institution: "metropolis" does not refer to an institution in this sentence.

5. Comparison: Based on the above analysis, "metropolis" is used to describe the physical location and its characteristics, without any shift in the semantic category or indication of people, activity, or institution.

6. Final answer: **Literal**

[1cm] **Science: They will be flown into the stadium with helicopters when a full **stadium** is cheering for them when they arrive.

Target Word: stadium Category: LOCATION Label: METONYMIC This implies a shift in the semantic meaning from location to the people in that location.

3. There is no indication that "stadium" refers to an activity in this context.

4. There is also no indication that "stadium" refers to an institution in this context.

5. Based on the analysis, the word "studatum" is used to represent the People in the Miami, which is a metonymic sense.

Final answer: **METONYMIC**

[1cm] **Ilocation**

To determine if the target word "metropolis" is used in a metonymic or literal sense, let's follow the steps:

1. Determine if there is a shift of the semantic category: The word "metropolis" is a location, and it refers to a large city, which is a type of location. There is no apparent shift in the semantic category.

2. Check if the target word indicates the PEOPLE of the location: In this sentence, "metropolis" does not indicate the people of the location. It describes the location itself as a large, Yiddish-speaking city.

3. Check if the target word tokens to the Activity: - There is no indication that "metropolis" represents an activity in this sentence.

4. Check if the target word location refers to an institution. - "Metropolis" does not refer to an institution in this context.

5. Comparison: Based on the above analysis, "metropolis" is used to describe the physical position and its characteristics, without any shift in the semantic category or indication of people, activity, or institution.

6. Final answer: **Literal**

[1cm] **Science: They will be flown into the stadium with helicopters when a full **stadium** is cheering for them when they arrive.

[MISSING_PAGE_POST]





**Example Information**

[10pt] **Llama-CoT-2S Output**

[10pt] **GPT-4o Output**

[10pt] **Container**

[10pt] **General**

To determine if the target word "crowd"in the sentences is used in a metonymic sense or literal sense, let's literal sense, let's break it down:**

1. Is the target word "crowd" still used in the sense of a container? No, a crowd is a group of people, not a container. Since the target word is not used as a container, we can conclude that it's not metonymic in the context of a container. However, we’ll follow the steps for clarity.

2. Determine if there is a shift of the semantic category: Yes, the word "crowd" refers to a group of people, not a container.

3. If there is a shift, has the semantic meaning of the word shifted from container to content or process? Not applicable, as "crowd" is not a container.

4. Does the target word refer to another entity? Yes, the target word "crowd" refers to a group of people.

5. Not applicable, as the content is not present in the sentence in relation to a container.

6. Not applicable, as the target word is not a container.

7. Final answer: **literal**




**Example Information**

[1] **Llama-CoT-2S Output**

[2] **GPT-4o Output**

[3] **Container To determine if the sentence is metonymic, let's go through the steps:**

**the sentence is used in a metonymic sense or literal sense, we'll follow the steps:**

**Stewart Friesen de Fran out of fuel.**

**Target Word: truck**

**The target word "truck" refers to a**

**can out of fuel.**

**Target Word: truck**

**Category: possessed Label:**

**Label:**

**non-metonymic**

**Non-metonymic**

**To determine if the target word "truck" in the sentence is used in a metonymic sense or literal sense, we'll follow the steps:**

**the target word "truck" refers to a**

**an out of fuel.**

**Target Word: truck**

**The target word "truck" refers to another entity, which is the driver or the team participating in the event.**

**The content (the entity participating in the event) is not explicitly present in the sentence, but it is implied.**

**Comparing the original meaning of "truck" as a container (vehicle) and its shifted meaning to content (the entity participating in the event), we can see that there is a**

**Then answer: netonymic**

**The output (the entity participating as a container (vehicle) and its shifted meaning to content (the entity participating in the event), we can see that there is a**

**Then answer: netonymic**

**The output (the entity participating in the event) is not explicitly present in the sentence, but it is implied.**

**Comparing the original meaning of "truck" as a container (vehicle) and its shifted meaning to content (the entity participating in the event), we can see that there is a**

**Then answer: netonymic**

**Afterward: netonymic**

[3] **If the change in semantic meaning is from container to content, is the content already present in the sentence? - Since no such shift has occurred, this step is not applicable.**

* There is no metonymic shift in meaning. The word "truck" is used literally to refer to the vehicle. Final answer: The word "truck" is used in its literal sense as a vehicle.**

**Final answer: LTTERAL**

[5] **In particular, the target word "riftle" generally refers to a firearm used for shooting. In the given sentence, it is used in the phrase "wielding a rifle to defend a house from German partroopers."**

**Algorithm 1** _The target word "truck" refers to a**

**Algorithm 1** _The target word "truck" refers to a**

**Algorithm 2** _The target word "truck" refers to a**

**Algorithm 3** _The target word "truck" refers to a**

**Algorithm 4** _Does the word refer to the product of the product? Yes, the word "rifle" refers to the product itself, not the producer._

**Once the word refer to the product of the product? No, the word "rifle" does not refer to the product of the producer.**

**Algorithm 5** _The target word "truck" refers to the product of the product of the producer._

**Algorithm 6** _Final answer: METERAL_

[5] **In particular, the target word "truck" refers to the product itself, not the producer._

**Algorithm 7** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 8** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 9** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 10** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 11** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 12** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 13** _The target word "truck" refers to the product itself, not the producer._

**Algorithm 14** _Does the word refer to the product of the producer? Yes, the word "rifle" refers to the product of the producer, in this case, the firearm used by the character._

**Algorithm 15** _Use this comparison to determine if there is a metonymy in the sentence: Since the world "rifle" refers to the product of the producer, and not the producer themselves, this is a case of metonymy._

**Algorithm 16** _Final answer: METERAL_