# Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method

arXiv: https://arxiv.org/abs/2401.06782 · HTML: https://mlsanity.com/paper/2401.06782/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model](https://mlsanity.com/paper/1906.02124/) (2019)
- [Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism](https://mlsanity.com/paper/2303.03165/) (2023)

## Built on by (most cited first, 18 of 18)

- [Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques](https://mlsanity.com/paper/2403.05801/) (2024)
- [Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data](https://mlsanity.com/paper/2402.09830/) (2024)
- [Research on feature fusion and multimodal patent text based on graph attention network](https://mlsanity.com/paper/2505.20188/) (2025)
- [Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management](https://mlsanity.com/paper/2402.17216/) (2024)
- [Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations](https://mlsanity.com/paper/2402.16035/) (2024)
- [Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization](https://mlsanity.com/paper/2403.07905/) (2024)
- [Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research](https://mlsanity.com/paper/2402.16038/) (2024)
- [Enhanced User Interaction in Operating Systems through Machine Learning Language Models](https://mlsanity.com/paper/2403.00806/) (2024)
- [Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks](https://mlsanity.com/paper/2403.03165/) (2024)
- [Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis](https://mlsanity.com/paper/2403.08217/) (2024)
- [Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation](https://mlsanity.com/paper/2403.09718/) (2024)
- [Utilizing Deep Learning for Enhancing Network Resilience in Finance](https://mlsanity.com/paper/2402.09820/) (2024)
- [Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning](https://mlsanity.com/paper/2403.13619/) (2024)
- [Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content](https://mlsanity.com/paper/2409.00269/) (2024)
- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations](https://mlsanity.com/paper/2403.02760/) (2024)
- [Measuring Information Distortion in Hierarchical Ultra long Novel Reconstruction:The Optimal Expansion Ratio](https://mlsanity.com/paper/2505.12572/) (2025)
- [Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection](https://mlsanity.com/paper/2507.18952/) (2025)

## Abstract (arXiv)

In the realm of patent document analysis, assessing semantic similarity between phrases presents a significant challenge, notably amplifying the inherent complexities of Cooperative Patent Classification (CPC) research. Firstly, this study addresses these challenges, recognizing early CPC work while acknowledging past struggles with language barriers and document intricacy. Secondly, it underscores the persisting difficulties of CPC research. To overcome these challenges and bolster the CPC system, This paper presents two key innovations. Firstly, it introduces an ensemble approach that incorporates four BERT-related models, enhancing semantic similarity accuracy through weighted averaging. Secondly, a novel text preprocessing method tailored for patent documents is introduced, featuring a distinctive input structure with token scoring that aids in capturing semantic relationships during CPC context training, utilizing BCELoss. Our experimental findings conclusively establish the effectiveness of both our Ensemble Model and novel text processing strategies when deployed on the U.S. Patent Phrase to Phrase Matching dataset.