# Research on a Framework for Chinese Argot Recognition and Interpretation by Integrating Improved MECT Models

## TL;DR

This paper introduces new frameworks to recognize and interpret Chinese argots used in underground industries, aiding law enforcement in detecting illegal activities.

## Contribution

The novel CSRMECT and LLMResolve frameworks improve argot recognition and interpretation using MECT models and large language models.

## Key findings

- CSRMECT achieves a 10% higher F1 score for argot recognition compared to existing models.
- LLMResolve improves interpretation accuracy by 4% over current methods.
- Vector information entropy correlates with model performance in argot recognition.

## Abstract

In underground industries, practitioners frequently employ argots to communicate discreetly and evade surveillance by investigative agencies. Proposing an innovative approach using word vectors and large language models, we aim to decipher and understand the myriad of argots in these industries, providing crucial technical support for law enforcement to detect and combat illicit activities. Specifically, positional differences in semantic space distinguish argots, and pre-trained language models’ corpora are crucial for interpreting them. Expanding on these concepts, the article assesses the semantic coherence of word vectors in the semantic space based on the concept of information entropy. Simultaneously, we devised a labeled argot dataset, MNGG, and developed an argot recognition framework named CSRMECT, along with an argot interpretation framework called LLMResolve. These frameworks leverage the MECT model, the large language model, prompt engineering, and the DBSCAN clustering algorithm. Experimental results demonstrate that the CSRMECT framework outperforms the current optimal model by 10% in terms of the F1 value for argot recognition on the MNGG dataset, while the LLMResolve framework achieves a 4% higher accuracy in interpretation compared to the current optimal model.The related experiments undertaken also indicate a potential correlation between vector information entropy and model performance.