EPO绿色塑料CodeFest解决方案：使用深度学习对绿色塑料相关专利进行分层多标签分类
Solution for the EPO CodeFest on Green Plastics: Hierarchical multi-label classification of patents relating to green plastics using deep learning
摘要：
本研究的目的是对绿色塑料相关技术的专利进行多标签专利分级。这是一个新兴领域，目前没有分类方案，因此没有标记数据可用，使得这项任务特别具有挑战性。我们首先提出了一个分类方案，这项技术和一种方法来学习一个机器学习模型，将专利分类到建议的分类方案。为了实现这一目标，我们提出了一种自动为专利分配标签的策略，以创建一个标记的训练数据集，该数据集可用于在监督学习设置中学习分类模型。使用所述训练数据集，我们提出了两个分类模型，一个SciBERT神经网络（SBNN）模型和一个SciBERT分层神经网络（SBHNN）模型。这两种模型都使用BERT模型作为特征提取器，并在其上使用神经网络作为分类器。我们进行了广泛的实验和报告共同评估指标，这个具有挑战性的分类问题。实验结果验证了我们的方法的有效性，并表明我们的模型设置了一个非常强大的基准这个问题。我们还通过可视化训练模型给出的单词重要性来解释我们的模型，这表明该模型能够提取输入文档的高级语义信息。最后，我们强调了我们的解决方案如何满足EPO CodeFest的评估标准，并概述了未来工作的可能方向。我们的代码已在https://github.com/epo/CF22-Green-Hands上提供
英文摘要：
This work aims at hierarchical multi-label patents classification for patents disclosing technologies related to green plastics. This is an emerging field for which there is currently no classification scheme, and hence, no labeled data is available, making this task particularly challenging. We first propose a classification scheme for this technology and a way to learn a machine learning model to classify patents into the proposed classification scheme. To achieve this, we come up with a strategy to automatically assign labels to patents in order to create a labeled training dataset that can be used to learn a classification model in a supervised learning setting. Using said training dataset, we come up with two classification models, a SciBERT Neural Network (SBNN) model and a SciBERT Hierarchical Neural Network (SBHNN) model. Both models use a BERT model as a feature extractor and on top of it, a neural network as a classifier. We carry out extensive experiments and report commonly evaluation metrics for this challenging classification problem. The experiment results verify the validity of our approach and show that our model sets a very strong benchmark for this problem. We also interpret our models by visualizing the word importance given by the trained model, which indicates the model is capable to extract high-level semantic information of input documents. Finally, we highlight how our solution fulfills the evaluation criteria for the EPO CodeFest and we also outline possible directions for future work. Our code has been made available at https://github.com/epo/CF22-Green-Hands
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
