This article proposes the Conventional and Novel Metaphor Identification Procedure (CNMIP) for Mandarin Chinese and applies this replicable protocol to annotate the VUPMC dataset, a new Political Metaphor Corpus developed at VU University Amsterdam. The VUPMC corpus contains three Chinese political genres (Policy Documents, Remarks, News Reports) and includes over 220,000 tokens of concordance sentences for the node word 贸易 ‘trade’. The corpus analysis shows that 6.64% of lexical units in the VUPMC dataset are used as metaphor-related words (MRWs) to frame trade (e.g., using ‘war’ to frame trade as a war). Further tests show that distributions of MRWs differ significantly across genres and Parts of Speech. Similarities in MRW distributions between the VUPMC and other datasets confirm the reliability of the CNMIP procedure. The differences, however, highlight the methodological advances in manual annotation of conventional and novel MRWs as well as the distinctive features of Chinese political genres. The VUPMC dataset serves as a valuable language resource for computational detection of Chinese conventional and novel metaphors.

Although recent advances have focused on detecting metaphors, existing models generally treat them as static entities. There has been little research into identifying dynamic metaphors in discourse. This article addresses this gap by focusing on metaphoricity cues: Linguistic signals that may indicate the activation of metaphoric meaning in different discourse contexts. This study examines the ability of OpenAI’s O-series models (O4-mini, O4-mini-high and O3) in detecting five metaphoricity cues in the U.S. trade discourse, including cues of explicit mapping, emphasis, marking, repetition and novelisation. Research results show that the models performed best on repetition and emphasis, while novelisation was the most difficult cue to detect.

Current approaches to computational metaphor processing typically incorporate static representations of metaphor. We aim to show that this limits the coverage of such systems. We take insights from dynamic metaphor theory and discuss how existing computational models of metaphor might benefit from representing the dynamics of metaphor when applied to the analysis of conflicting discourse. We propose that a frame-based approach to metaphor representation based on the model of YinYang Dynamics of Metaphoricity (YYDM) would pave the way to more comprehensive modeling of metaphor. In particular, the metaphoricity cues of the YYDM model could be used to address the task of dynamic metaphor identification. Frame-based modeling of dynamic metaphor would facilitate the computational analysis of perspectives in conflicting discourse, with potential applications in analyzing political discourse.