DAGS : A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition (2025)

Abstract: Current metaphor recognition mainly rely on Metaphor Detection Theory (MDT), such as the Metaphor Identification Procedure, which recognizes metaphors by comparing the basic meaning of target word with context meaning. Existing studies have gradually adopted literal annotations to model basic meanings, rejecting the aggregated meanings of target words. However, these methods ignore the problem of interference caused by literal annotations, and do not make full use of semantic expression relations of MDT, making the models difficult to detect and generalize. To address these challenges, we propose a dependency-based D ual- A ttention and G lobal S emantic Improvement (DAGS) framework. DAGS first extracts literal annotations of target words as basic meaning from several mainstream corpora. Then, we apply dependency tree and dual-attention while filtering on input sentences and basic meanings. Finally, we improve the MDT to further consider the global semantic relationship on contexts. The DAGS can not only extract features from multiple information sources but alsoeffectively removes redundancy, while focusing on mission-critical information. We achieve state-of-the-art on several mainstream metaphor datasets (e.g., VUA ALL, VUAverb, TroFi and PSUCMC), which suggests that filtering and global semantic improvement of contexts is crucial for enhancing metaphor recognition performance.

## introduction as a universal linguistic phenomenon, metaphor is widely found in daily communication, literary works and media reports. according to the conceptual metaphor theory (cmt), metaphor is defined as a mapping between the source domain and target domain, i.e., the expression of a deeper meaning beyond its literal meaning through one or more words @xcite . for example, in "he hit the nail on the head in the meeting." the "hit" means pinpointing the core point of issue rather than actual body part that was struck. metaphor recognition plays an important role in cognition and communication, and is widely used in nlp tasks, such as machine translation @xcite , paraphrase generation @xcite and sentiment analysis @xcite . several strategies have been proposed to detect metaphors. for example, @xcite ; @xcite models the complete sentence context, whereas @xcite introduces the metaphor identification procedure (mip) @xcite and selectional preference violation (spv) @xcite . for mip, a word can be identified as metaphor when its literal meaning contrasts with its context meaning of a given sentence. to spv, the target word is metaphorical when it occurs less frequently in its context or is semantically mismatched @xcite . for example, in "he ignites inspiration", the contextual meaning of "ignites" is "stimulates creativity", which is different from the literal meaning of "ignites a flame". moreover, the "ignite" is metaphorical, which is rare in the context of "inspiration". however, the challenge of accurately handling contextual noise and extracting the basic meanings of target words remains an open research question. recently, @xcite considers only part of the contextual noise problem and still has redundant information and aggregated meanings (i.e., proximate meaning). @xcite ; @xcite propose an improved method for mip by abandoning the traditional aggregated meaning of the target word and taking the literal annotation as the basic meaning (e.g., replacing "attack" with "february the germans attacked verdun."). they argue that the use of aggregated meanings weakens the validity of mip and spv. although transforming basic meanings in mip by adding context may help in metaphor recognition @xcite , it may also introduce redundant interference that prevents the model from focusing on key semantic information. furthermore, these methods fail to fully utilize the semantic relationships of contexts when using mdt. current spvs only consider the difference between the target word and a single context, while ignoring its relationship with other contexts (e.g., literal annotations). for mip, these methods also lack the understanding of the global context. therefore, filtering redundancy in context, focusing on task-relevant information and improving semantic relationships remain serious challenges for metaphor recognition. to solve the above problems, we propose a new metaphor recognition framework, which combines dependency-based dual-attention and semantic improvement, namely dags. first, we utilizes gpt-4o (version gpt-4o-2024-08-06) to obtain literal annotations of target words from mainstream metaphor corpora, serving as basic meanings. subsequently, we construct a dependency tree structure and perform dependency analysis, pruning input sentences and basic meanings simultaneously. meanwhile, dags introduce a dual-attention module to further extract contextual task-relevant information. finally, dags improves spv and mip, named global-spv (g-spv) and global-mip (g-mip), respectively, to further detect metaphors by considering the semantic relations of contexts. additionally, dags also integrates multiple linguistic features, such as part-of-speech (pos), position and local context. in summary, our contributions are as follows: ## related work earlier studies used aggregated meanings as a substitute for basic meanings. however, zhang and liu (2022); li et al. (2023a) argued that aggregated meanings violated the mip principle and instead began using literal annotations as the basic meaning. building on this, tian et al. (2024b) continued to use literal annotations to construct sets of example sentences to recognize metaphors. but these approaches fail to adequately consider redundancy and key information in multiple contexts. previously, wang et al. (2023) mitigated the noise in the context by optimizing dependency parsing despite the use of aggregated meanings, which provides important inspiration for dual-dependencies in dags. recently, jia and li (2024) introduced internal and external semantics and multiple metaphor recognition mechanisms to learn differences between sentences. similarly, qiao et al. (2024) modeled literal meaning uncertainty in mip through a density matrix. and uduehi and bunescu (2024) used spv and mip to construct expectation and realization components to evaluate the meaning of target words. while wang et al. (2025) refined the conceptual knowledge of inter-word relationships and explored the similarity of cross-domain concepts. these studies further demonstrate the importance of semantics in metaphor detection, but most of the approaches ignore the role of global semantic information. in addition, zhang and liu (2022); li et al. (2023a ) used aggregated meanings on some of the data despite considering literal annotations. in recent years, llm has shown significant potential in metaphor research. for example, @xcite employed gpt-3 to detect metaphor expressions in a given sentence and predict their source domains. @xcite detected verb metaphors using gpt-3.5, aided by literal collocations and entailment relationship analysis. similarly, @xcite ; tian et al. (2024a) explored the metaphor identification and reasoning capabilities of multiple llms. we believe that this problem can be solved by combining multiple sources and gpt-4o to extract literal annotations. ## method we propose a new metaphor recognition method, dags, that combines dependency-based dualattention and semantic improvement. the figure 1 shows an overview of our framework. ## pos experiments for vua all, we conduct fine-grained experiments based on pos (adjective, verb, noun, and adverb). ## zero-shot transfer we design zero-shot transfer experiments across english datasets. specifically, we train on vua all and test on entire trofi and moh-x. the results are shown in ## semantic improvement to evaluate the effectiveness of semantic improvement, we conduct comparative experiments on vuaverb. for each set of comparative results, we provide a data visualization, which is shown in figure 3 . g-spv and g-mip show significant enhancement in both models. for example, for dags, g-spv enhances f1 from 76.5% to 78.1%, while g-mip improves it from 77.0% to 77.9%. in addition, g-spv and g-mip not only have independent effects in terms of their respective semantic improvement, but also show synergistic effects. these results suggest that incorporating global semantics can further utilize the semantic information, especially in complex language environments. ## ablation the ablation results for each module are shown in ## sentence length experiments sentence length variations affect the focus of model's attention and interference degree. to explore this phenomenon in depth, we design experiments to analyze the effect of sentence length on model performance. as shown in the results show that dags can effectively filter out irrelevant contexts in sentences and basic meanings, and its performance improvement shows a significant positive correlation with sentence length (e.g., f1 with 82.9% > 81.2% > 77.8%). ## case study see ## conclusion in this paper, we propose a novel metaphor recognition model, dags, which introduces dependencybased dual-attention module to filter context and focus on key content. and then, dags further enhances metaphor recognition through global semantic. experiments show that dags achieves state-of-the-art performance on metaphor recognition compared to existing models.