An Implicit Transcript Enhancement Method for Unbalanced Crimes Based on PolyLoss Function

doi:10.11871/jfdc.issn.2096-742X.2026.01.013

Abstract

Abstract:

[Objective] This study aims to address the imbalance issue in the criminal cant dataset and further improve the classification performance of criminal cant texts. [Method] For long texts, we present SimPoly, while for short texts, EDAPoly is proposed. [Result] Experimental results demonstrate that SimPoly and EDAPoly significantly improve classification model performance on imbalanced criminal cant datasets, achieving notable gains in accuracy, recall, and F1-score compared to baseline methods without text augmentation. [Conclusion] The proposed method not only provides an effective solution for the practical application of criminal cant recognition technology, but also offers new ideas and support for similar imbalanced text classification tasks.

Key words: criminal cant, text augmentation, loss function, unbalanced text processing

LI Wenbang,YAN Jinghua,DONG Ze. An Implicit Transcript Enhancement Method for Unbalanced Crimes Based on PolyLoss Function[J]. Frontiers of Data and Computing, 2026, 8(1): 158-167, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.01.013.

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参数名称	参数值
Learning rate	1e-5
Epoch	3
pad_size	128
batch_size	128
hidden_size	768

Experiment stages	Model	Precision	Accuracy	Recall	F1
第一阶段	BERT	78.88%	65.52%	86.76%	74.66%
	BERT_RCNN	79.10%	67.16%	81.67%	73.71%
	ROBERTA	76.71%	62.41%	86.75%	72.60%
	ERNIE	78.53%	64.43%	88.22%	74.47%
	ERNIE_RCNN	78.61%	65.55%	82.49%	73.05%
第二阶段	BERT	83.97%	78.82%	92.51%	85.12%
	BERT_RCNN	83.97%	79.51%	91.14%	84.93%
	ROBERTA	82.58%	77.52%	91.81%	84.07%
	ERNIE	83.44%	77.83%	93.38%	84.90%
	ERNIE_RCNN	83.53%	78.92%	91.37%	83.64%

Mode	γ	Precision	Accuracy	Recall	F1
BERT	0.8	84.00%	80.59%	90.19%	85.12%
	0.9	84.38%	80.47%	91.41%	85.59%
	1	84.44%	79.64%	93.15%	85.87%
	1.1	84.32%	80.07%	92.05%	85.64%
BERT_RCNN	0.8	84.11%	79.17%	93.27%	85.65%
	0.9	84.32%	79.19%	93.79%	85.87%
	1	84.06%	78.68%	94.08%	85.69%
	1.1	83.85%	80.95%	89.21%	84.88%
ROBERTA	0.8	81.41%	77.59%	89.52%	83.13%
	0.9	80.79%	76.63%	89.87%	82.72%
	1	81.79%	78.10%	89.52%	83.42%
	1.1	80.67%	75.59%	90.43%	82.35%
ERNIE	0.8	84.73%	79.66%	92.49%	85.60%
	0.9	84.59%	79.70%	92.01%	85.41%
	1	84.56%	79.75%	91.83%	85.36%
	1.1	84.20%	79.72%	90.93%	84.95%
ERNIE_RCNN	0.8	84.17%	79.49%	91.29%	84.98%
	0.9	84.03%	80.26%	89.42%	84.59%
	1	84.23%	79.39%	91.65%	85.08%
	1.1	84.44%	79.52%	91.95%	85.28%

Models	Precision	Accuracy	Recall	F1	γ	Epoch
BERT	84.44%	79.64%	93.15%	85.87%	1	3
BERT_RCNN	84.32%	79.19%	93.79%	85.87%	0.9	3
ROBERTA	81.79%	78.10%	89.52%	83.42%	1	3
ERNIE	84.73%	79.66%	92.49%	85.60%	0.8	3
ERNIE_RCNN	84.44%	79.52%	91.95%	85.28%	1.1	3

Experimentstages	Models	Precision	Accuracy	Recall	F1
第二阶段	BERT	5.09%	13.30%	5.75%	10.46%
	BERT_RCNN	4.87%	12.35%	9.47%	3.26%
	ROBERTA	5.87%	15.11%	4.43%	11.47%
	ERNIE	4.91%	13.40%	5.16%	10.30%
	ERNIE_RCNN	4.92%	13.37%	8.88%	10.59%
第三阶段	BERT	0.47%	0.82%	0.64%	0.75%
	BERT_RCNN	0.35%	-0.32%	2.65%	0.94%
	ROBERTA	-0.79%	0.58%	-2.29%	-0.65%
	ERNIE	1.29%	1.83%	-0.89%	0.70%
	ERNIE_RCNN	0.91%	-0.40%	0.58%	1.64%