基于常识知识和情绪知识语义不一致的讽刺检测方法OA

Sarcasm involves semantic incongruity between literal meanings and intended emotions.Accurate modeling of such incon-gruity is critical for sarcasm detection.Semantic incongruity occurs not only within textual content but also between the text and ex-ternal commonsense and emotional knowledge.Previous studies typically considered semantic incongruity either between text and commonsense or text and emotional knowledge separately,neglecting their joint influence.This study proposed a sarcasm detection model named Semantic Incongruity Incorporating Commonsense and Emotional Knowledge(SIICE).The SIICE model employed an attention mechanism to model semantic incongruities among textual semantics,commonsense knowledge,and emotional knowl-edge.SIICE consisted of four modules:a textual semantic incongruity module,a commonsense semantic incongruity module,an emotional semantic incongruity module,and a fusion prediction module.The first three modules utilized attention mechanisms to capture semantic incongruities within the text,between text and commonsense knowledge generated by COMET(Commonsense Transformers),and between text and emotional knowledge represented via the VAD(Valence-Arousal-Dominance)emotional mod-el,respectively.The fusion module integrated these three representations for sarcasm detection.Experiments were conducted on three benchmark datasets:Twitter(Ptáček),IAC-V1,and IAC-V2.SIICE achieved superior performance compared to baseline mod-els,obtaining F1-scores of 0.870 3,0.704 9,and 0.783 5,respectively.Ablation experiments indicated that simultaneous modeling of semantic incongruities involving text,commonsense,and emotional knowledge effectively enhances sarcasm detection performance.