# Attention Mechanism and Context Modeling System for Text Mining Machine Translation

arXiv: https://arxiv.org/abs/2408.04216 · HTML: https://mlsanity.com/paper/2408.04216/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space](https://mlsanity.com/paper/2309.14616/) (2023)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [AttentionViz: A Global View of Transformer Attention](https://mlsanity.com/paper/2305.03210/) (2023)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Application of Multimodal Fusion Deep Learning Model in Disease Recognition](https://mlsanity.com/paper/2406.18546/) (2024)
- [Advanced Financial Fraud Detection Using GNN-CL Model](https://mlsanity.com/paper/2407.06529/) (2024)
- [An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation](https://mlsanity.com/paper/2406.01605/) (2024)
- [Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis](https://mlsanity.com/paper/2406.18548/) (2024)
- [Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis](https://mlsanity.com/paper/2405.17459/) (2024)
- [Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network](https://mlsanity.com/paper/2406.08837/) (2024)
- [Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules](https://mlsanity.com/paper/2406.13205/) (2024)
- [Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique](https://mlsanity.com/paper/2406.18549/) (2024)
- [Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning](https://mlsanity.com/paper/2406.08864/) (2024)
- [Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks](https://mlsanity.com/paper/2405.17460/) (2024)
- [Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis](https://mlsanity.com/paper/2405.20603/) (2024)
- [Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning](https://mlsanity.com/paper/2406.08838/) (2024)
- [Advanced Multimodal Deep Learning Architecture for Image-Text Matching](https://mlsanity.com/paper/2406.15306/) (2024)
- [Enhanced Bilingual Evaluation Understudy](https://mlsanity.com/paper/1509.09088/) (2015)

## Built on by (most cited first, 15 of 15)

- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving](https://mlsanity.com/paper/2409.03320/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
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- [Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment](https://mlsanity.com/paper/2409.17909/) (2024)
- [Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications](https://mlsanity.com/paper/2410.08553/) (2024)
- [User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data](https://mlsanity.com/paper/2505.06305/) (2025)
- [Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training](https://mlsanity.com/paper/2409.04707/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/2408.05860/) (2024)
- [Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model](https://mlsanity.com/paper/2410.09923/) (2024)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)

## Abstract (arXiv)

This paper advances a novel architectural schema anchored upon the Transformer paradigm and innovatively amalgamates the K-means categorization algorithm to augment the contextual apprehension capabilities of the schema. The transformer model performs well in machine translation tasks due to its parallel computing power and multi-head attention mechanism. However, it may encounter contextual ambiguity or ignore local features when dealing with highly complex language structures. To circumvent this constraint, this exposition incorporates the K-Means algorithm, which is used to stratify the lexis and idioms of the input textual matter, thereby facilitating superior identification and preservation of the local structure and contextual intelligence of the language. The advantage of this combination is that K-Means can automatically discover the topic or concept regions in the text, which may be directly related to translation quality. Consequently, the schema contrived herein enlists K-Means as a preparatory phase antecedent to the Transformer and recalibrates the multi-head attention weights to assist in the discrimination of lexis and idioms bearing analogous semantics or functionalities. This ensures the schema accords heightened regard to the contextual intelligence embodied by these clusters during the training phase, rather than merely focusing on locational intelligence.