Metaphor detection model based on linguistic multi-incongruity

doi:10.11772/j.issn.1001-9081.2024121797

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (12): 3829-3838.DOI: 10.11772/j.issn.1001-9081.2024121797

• Artificial intelligence • Previous Articles Next Articles

Metaphor detection model based on linguistic multi-incongruity

Tianlong ZHENG¹^,²^,³, Rui DONG¹^,²^,³, Yating YANG¹^,²^,³, Bo MA¹^,²^,³, Lei WANG¹^,²^,³, Xi ZHOU¹^,²^,³

^1.Xinjiang Technical Institute of Physics and Chemistry，Chinese Academy of Sciences，Urumqi Xinjiang 830011，China
^2.University of Chinese Academy of Sciences，Beijing 100049，China
^3.Xinjiang Laboratory of Minority Speech and Language Information Processing （ Chinese Academy of Sciences），Urumqi Xinjiang 830011，China

Received:2024-12-23 Revised:2025-03-06 Accepted:2025-03-13 Online:2025-03-24 Published:2025-12-10
Contact: Rui DONG
About author:ZHENG Tianlong， born in 2002， M. S. candidate. His research interests include metaphor detection.
DONG Rui， born in 1985， Ph. D.， research fellow. His research interests include natural language processing， metaphor detection.
YANG Yating， born in 1985， Ph. D.， research fellow. Her research interests include multilingual intelligent information processing.
MA Bo， born in 1984， Ph. D.， research fellow. His research interests include multilingual intelligent information processing.
WANG Lei， born in 1974， Ph. D.， research fellow. His research interests include multilingual intelligent information processing.
ZHOU Xi， born in 1978， Ph. D.， research fellow. His research interests include big data analysis.
Supported by:
Key Project of Xinjiang Uygur Autonomous Region Natural Science Foundation(2024D01D29);Xinjiang Uygur Autonomous Region “Tianshan Talents” Training Program(2022TSYCCX0059);Project of Youth Innovation Promotion Association, Chinese Academy of Sciences(Y2021112);Xinjiang Uygur Autonomous Region Key Research and Development Program(2023B03024)

基于语言学多重不一致性的隐喻检测模型

郑天龙¹^,²^,³, 董瑞¹^,²^,³, 杨雅婷¹^,²^,³, 马博¹^,²^,³, 王磊¹^,²^,³, 周喜¹^,²^,³

^1.中国科学院新疆理化技术研究所，乌鲁木齐 830011
^2.中国科学院大学，北京 100049
^3.新疆民族语音语言信息处理实验室（中国科学院），乌鲁木齐 830011

通讯作者: 董瑞
作者简介:郑天龙（2002—），男，安徽阜阳人，硕士研究生，主要研究方向：隐喻检测
董瑞（1985—），男，山东威海人，研究员，博士，CCF高级会员，主要研究方向：自然语言处理、隐喻检测
杨雅婷（1985—），女，新疆奇台人，研究员，博士，CCF高级会员，主要研究方向：多语言智能信息处理
马博（1984—），男，辽宁鞍山人，研究员，博士，CCF高级会员，主要研究方向：多语言智能信息处理
王磊（1974—），男，河南南阳人，研究员，博士，主要研究方向：多语言智能信息处理
周喜（1978—），男，湖南双峰人，研究员，博士，CCF高级会员，主要研究方向：大数据分析。
基金资助:
新疆维吾尔自治区自然科学基金重点项目(2024D01D29);新疆维吾尔自治区自然科学基金重点项目(2022D01D04);新疆维吾尔自治区自然科学基金重点项目(2022D01D81);新疆维吾尔自治区“天山英才”培养计划项目(2022TSYCCX0059);新疆维吾尔自治区“天山英才”培养计划项目(2023TSYCCX0044);新疆维吾尔自治区“天山英才”培养计划项目(2023TSYCCX0041);中国科学院青年创新促进会项目(Y2021112);中国科学院青年创新促进会项目(Y2023118);中国科学院青年创新促进会项目(2021436);新疆维吾尔自治区重点研发计划项目(2023B03024)

Abstract

Abstract:

A metaphor detection model based on linguistic multi-incongruity was proposed to tackle the metaphor occurrence problem caused by the incongruity between the target sentence meaning and the core meaning of the target word in a specific context where a target word has multiple semantic meanings （polysemy）， which is ignored by the existing metaphor detection research. Firstly， in the feature encoding module， two separate encoders were employed to encode the feature information such as the target sentence meaning， the core meaning of the target word， and its contextual meaning. Then， in the multi-incongruity modeling module， three linguistic methods — Selectional Preference Violation （SPV）， Metaphor Identification Procedure （MIP）， and Semantics Usage Comparison （SUC） — were utilized to conduct unified modeling of incongruity features. Finally， metaphor detection was performed through a metaphor identification module. Furthermore， to validate Chinese metaphor detection performance， a Chinese word-level metaphor detection dataset named META-ZH was constructed through a data annotation method of combining LoRA （Low-Rank Adaptation） fine-tuned Large Language Model （LLM） with manual correction. Experimental results show that the proposed model achieves F1 values improvement of 0.8， 1.3， 1.5， and 2.3 percentage points， respectively， compared to the optimal baseline model on the VUA All， VUA Verb， MOH-X， and META-ZH metaphor detection datasets. It can be seen that the proposed model enhances performance in metaphor detection by fully utilizing linguistic multi-incongruity.

Key words: metaphor detection, Multi-incongruity Network (MulNet), linguistic method, Large Language Model (LLM), LoRA (Low-Rank Adaptation) fine-tuning

摘要：

针对现有隐喻检测研究忽略了目标词在特定语境中存在多种语义（一词多义）时目标语句句义和目标词基本义不一致引起的隐喻发生问题，提出一种基于语言学多重不一致性的隐喻检测模型。首先，在特征编码模块，使用2个独立的编码器编码目标语句句义、目标词基本义和语境义等特征信息；其次，在多重不一致性建模模块，使用选择偏好违背（SPV）、隐喻识别程序（MIP）和语义用法对比（SUC）这3个语言学方法对多重不一致性特征进行统一建模；最后，利用隐喻识别模块进行隐喻检测。此外，通过LoRA（Low-Rank Adaptation）微调的大语言模型（LLM）和人工矫正结合的数据标注方法构建一个中文词级隐喻检测数据集META-ZH，以验证中文隐喻检测性能。实验结果表明，所提模型在VUA All、VUA Verb、MOH-X和META-ZH隐喻检测数据集上，对比最优基线模型，F1值分别提升了0.8、1.3、1.5和2.3个百分点。可见，该模型能够充分利用语言学多重不一致性有效提高隐喻检测性能。

关键词: 隐喻检测, 多重不一致性网络, 语言学方法, 大语言模型, LoRA微调

CLC Number:

TP391.1

Tianlong ZHENG, Rui DONG, Yating YANG, Bo MA, Lei WANG, Xi ZHOU. Metaphor detection model based on linguistic multi-incongruity[J]. Journal of Computer Applications, 2025, 45(12): 3829-3838.

郑天龙, 董瑞, 杨雅婷, 马博, 王磊, 周喜. 基于语言学多重不一致性的隐喻检测模型[J]. 《计算机应用》唯一官方网站, 2025, 45(12): 3829-3838.

Figures/Tables 12

Tab. 1 Examples of metaphor

3）在生活的雪坡上滑雪

一定要慢点，小心地滑。

SUC

Fig. 1 SPV， MIP and SUC linguistic methods

Fig. 2 Structure of MulNet model

Fig. 3 Flow of META-ZH dataset construction

Tab. 2 Descriptive statistics of datasets

数据集	目标语句数	目标词数	隐喻样本占比/%	目标语句平均长度（词数）
MOH-X	647	647	48.69	8.00
VUA All	10 488	205 425	11.58	19.55
VUA Verb	11 699	23 113	28.36	20.52
TroFi	3 737	3 737	43.54	28.30
VUA All_Genre	2 679	50 175	12.44	18.71
VUA All_POS	8 065	25 818	15.35	21.39
META-ZH	5 491	5 496	92.78	18.64

Tab. 3 Experimental results on MOH-X， VUA All， VUA Verb and META-ZH datasets

模型	MOH-X				VUA All				VUA Verb				META-ZH
模型	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec
RNN_ELMo	75.6	77.2	79.1	73.5	72.6	93.1	71.6	73.6	69.7	81.4	68.2	71.3	—	—	—	—
RNN_BERT	78.2	78.1	75.1	81.8	71.7	92.9	71.5	71.9	69.0	80.7	66.7	71.5	76.5	93.9	77.0	76.0
RNN_HG	79.8	79.7	79.7	79.8	74.0	93.6	71.8	76.3	70.8	82.1	69.3	72.3	—	—	—	—
RNN_MHCA	80.0	79.8	77.5	83.1	74.3	93.8	73.0	73.0	70.5	81.8	66.3	75.2	—	—	—	—
MUL_GCN	79.6	79.9	79.7	80.5	75.1	93.8	74.8	75.5	71.7	83.2	72.5	70.9	—	—	—	—
MelBERT	81.1	81.6	79.7	82.7	78.4	94.0	80.5	76.4	71.0	80.7	64.6	78.8	76.4	94.9	84.4	71.8
MisNet	82.5	83.1	83.2	82.5	77.5	94.7	82.4	73.2	72.4	84.4	77.0	68.3	80.4	95.3	83.6	77.9
MisNet_DeBERTa	85.0	85.6	86.2	84.6	70.3	91.7	63.2	79.1	70.4	82.2	70.3	70.5	80.0	95.0	81.8	78.4
CLCL	83.4	84.3	84.0	82.7	78.4	94.5	80.8	76.1	74.4	84.7	74.9	73.9	78.2	95.1	84.3	74.3
QMM	86.0	86.0	83.8	88.6	78.1	94.6	78.9	77.3	72.6	84.9	79.8	66.6	—	—	—	—
QMM_DeBERTa	85.7	86.2	85.5	86.3	78.8	94.7	77.9	79.8	74.8	84.9	74.7	74.9	—	—	—	—
MiniCPM3-4B（LoRA）	75.7	73.8	70.7	81.5	74.9	94.0	78.2	71.9	65.9	80.4	68.9	63.1	72.7	93.2	74.1	71.5
LLaMA3-8B（LoRA）	64.3	68.5	74.0	56.9	76.2	94.3	79.9	72.8	73.5	84.7	76.6	70.6	80.0	94.6	79.5	80.5
MulNet	87.2	87.2	85.1	89.7	79.6	95.1	82.1	77.2	76.1	85.1	73.2	79.2	82.7	95.8	86.1	80.0

Tab. 4 Experimental results on subdivision datasets VUA All_Genre and VUA All_POS

模型	Fiction				News				Conversation				Academic
模型	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec
RNN_ELMo	65.1	93.1	61.4	69.1	71.9	91.6	72.7	71.2	79.2	92.8	78.2	80.2	64.0	94.6	64.9	63.1
RNN_BERT	67.5	93.9	66.5	68.6	71.8	91.4	71.2	72.5	76.4	91.9	76.7	76.0	64.4	94.6	64.7	64.2
RNN_HG	67.5	93.4	61.8	74.5	74.1	91.9	71.6	76.8	79.6	92.7	76.5	83.0	67.8	94.8	63.6	72.5
RNN_MHCA	67.7	93.8	64.8	70.9	75.0	92.4	74.8	75.3	79.8	93.0	79.6	80.0	67.4	94.8	64.0	71.1
RoBERTa_SEQ	73.3	—	73.9	72.7	77.9	—	82.2	74.1	81.4	—	86.0	77.3	70.1	—	70.5	69.8
DeepMet	73.0	—	76.1	70.1	75.0	—	84.1	67.6	81.0	—	88.4	74.7	71.4	—	71.6	71.1
MelBERT	75.4	—	74.0	76.8	77.2	—	81.0	73.7	83.9	—	85.3	82.5	70.9	—	70.1	71.7
MisNet	76.0	95.5	74.5	77.5	79.2	94.1	82.6	77.0	83.8	94.5	85.1	82.5	71.9	95.7	71.8	72.2
MulNet	76.7	95.7	76.5	76.9	80.3	94.4	86.1	75.2	84.3	94.7	87.4	81.4	72.3	95.5	70.3	74.4
模型	Noun				Adjective				Verb				Adverb
模型	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec	F1	Acc	Prec	Rec
RNN_ELMo	60.4	—	59.9	60.8	58.3	—	56.1	60.6	69.9	—	68.1	71.9	59.7	94.8	67.2	53.7
RNN_BERT	59.9	88.6	63.3	56.8	54.7	88.3	58.1	51.6	69.5	87.9	67.1	72.1	62.9	94.8	64.8	61.1
RNN_HG	63.4	88.4	60.3	66.8	62.2	89.1	59.2	65.6	70.7	88.0	66.4	75.5	63.8	94.5	61.0	66.8
RNN_MHCA	63.2	89.4	69.1	58.2	61.6	89.5	61.4	61.7	70.7	87.9	66.0	76.0	63.2	94.9	66.1	60.7
RoBERTa_SEQ	66.6	—	76.5	59.0	63.7	—	72.0	57.1	74.8	—	74.4	75.1	70.1	—	77.6	63.9
DeepMet	65.4	—	76.5	57.1	63.3	—	79.0	52.9	73.3	—	78.8	68.5	72.3	—	79.4	66.4
MelBERT	70.7	—	75.4	66.5	64.4	—	69.4	60.1	75.1	—	74.2	75.9	74.6	—	80.2	69.7
MisNet	70.6	91.6	74.4	67.2	67.0	91.2	68.8	65.2	77.6	91.4	77.5	77.7	73.3	96.3	76.4	70.5
MulNet	71.7	92.2	78.3	66.2	69.4	92.3	75.9	64.0	77.8	91.5	77.1	78.6	76.0	96.7	79.7	72.5

Tab. 5 Zero-shot transfer experimental results on TroFi dataset

模型	F1	Acc	Prec	Rec
RoBERTa_SEQ	60.7	60.6	53.6	70.1
DeepMet	61.7	60.8	53.7	72.9
MelBERT	62.0	60.6	53.4	74.1
MrBERT	62.7	61.1	53.8	75.0
MisNet	63.1	61.2	53.8	76.2
RoPPT	63.3	61.6	54.2	76.2
MiceCL	62.9	61.5	54.2	75.0
MulNet	64.2	62.7	55.1	77.0

Tab. 6 Ablation experimental results of multi-incongruity features

消融	F1	Acc	Prec	Rec
w/o MIP	79.4	95.1	82.3	76.7
w/o SPV	79.4	95.0	81.9	77.1
w/o SUC	78.3	94.9	82.7	74.4
w/o POS	79.1	95.0	83.1	75.5
w/o Basic Usage	78.4	94.8	81.7	75.3
w/o Feature Embedding	78.7	94.9	81.7	76.0
MulNet	79.6	95.1	82.1	77.2

Tab. 7 Ablation experimental results of different feature ensemble methods in MulNet

消融	F1	Acc	Prec	Rec
w/o （ $z - v$ ； $z × v$ ）	78.8	94.9	81.9	75.8
w/o （ $z$ ； v； $z × v$ ）	78.9	94.9	82.0	76.1
w/o （ $z$ ； v； $z - v$ ）	79.2	95.0	82.4	76.2
w/o （ $z$ ； v ）	79.2	94.9	80.6	77.9
w/o （ $z × v$ ）	79.0	94.9	81.5	76.6
w/o （ $z - v$ ）	78.9	94.9	81.1	76.8
MulNet	79.6	95.1	82.1	77.2

Tab. 7 Ablation experimental results of different feature ensemble methods in MulNet

消融	F1	Acc	Prec	Rec
w/o （ $z - v$ ； $z × v$ ）	78.8	94.9	81.9	75.8
w/o （ $z$ ； v； $z × v$ ）	78.9	94.9	82.0	76.1
w/o （ $z$ ； v； $z - v$ ）	79.2	95.0	82.4	76.2
w/o （ $z$ ； v ）	79.2	94.9	80.6	77.9
w/o （ $z × v$ ）	79.0	94.9	81.5	76.6
w/o （ $z - v$ ）	78.9	94.9	81.1	76.8
MulNet	79.6	95.1	82.1	77.2

Fig. 4 t-SNE feature visualization on VUA All， VUA Verb， META-ZH and TroFi datasets

Tab. 8 Examples of metaphor detection in Chinese and English

语言	目标词	目标语句	真实值	预测值
中文	讹诈	1）新中国成立后，为了打破某些大国的核讹诈，发展国防核能工业，中央对原子能的原料资源铀矿的勘查非常重视。	隐喻	隐喻
	循	2）接着，我们循着喊声，又走进“小芙蓉”美容美发店。	非隐喻	非隐喻
	流动	3）在这种流动中，最使人感念的是父母的牺牲。	非隐喻	隐喻
	开启	4）正是这位智慧的老人最先开启国门，推船下海的。	隐喻	非隐喻
英文	hands	1） Money may not have changed hands.	隐喻	隐喻
	capacity	2） Five more incinerators currently proposed in various parts of the country will double capacity.	非隐喻	非隐喻
	bear	3） There are a number of points to bear in mind.	隐喻	隐喻
	vague	4） PEOPLE tend to look vague when you mention Poitou.	隐喻	非隐喻

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Metaphor detection model based on linguistic multi-incongruity

基于语言学多重不一致性的隐喻检测模型

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