Research on relation extraction from patent documents, a high-priority topic of natural language process in recent years, is of great significance to a series of patent downstream applications, such as patent content mining, patent retrieval, and patent knowledge base constructions. Due to lengthy sentences, crossdomain technical terms, and complex structure of patent claims, it is extremely difficult to extract open triples with traditional methods of Natural Language Processing (NLP) parsers. In this paper, we propose an Open Relation Extraction (ORE) approach with transforming relation extraction problem into sequence labeling problem in patent claims, which extract none predefined relationship triples from patent claims with a hybrid neural network architecture based on multihead attention mechanism. The hybrid neural network framework combined with Bi-LSTM and CNN is proposed to extract argument phrase features and relation phrase features simultaneously. The Bi-LSTM network gains long distance dependency features, and the CNN obtains local content feature; then, multihead attention mechanism is applied to get potential dependency relationship for time series of RNN model; the result of neural network proposed above applied to our constructed open patent relation dataset shows that our method outperforms both traditional classification algorithms of machine learning and the-state-of-art neural network classification models in the measures of Precision, Recall, and F1.

中文翻译：

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混合网络专利索赔中的开放关系提取

从专利文献中提取关系的研究是近年来自然语言处理的一个高度优先的话题，对于一系列专利下游应用（如专利内容挖掘，专利检索和专利知识库构建）具有重要意义。由于冗长的句子，跨领域技术术语以及专利权利要求的复杂结构，使用自然语言处理（NLP）解析器的传统方法提取开放三元组非常困难。在本文中，我们提出了一种开放关系提取（ORE）方法，将关系提取问题转换为专利权利要求中的序列标记问题，该方法使用基于多头注意机制的混合神经网络体系结构从专利权利要求中不提取预定义的关系三元组。提出了一种结合Bi-LSTM和CNN的混合神经网络框架，以同时提取参数短语特征和关系短语特征。Bi-LSTM网络获得长距离依赖特征，而CNN获得本地内容特征；然后，采用多头注意力机制获得RNN模型时间序列的潜在依赖关系。上面提出的神经网络应用于构建的开放专利关系数据集的结果表明，在Precision，Recall和F1方面，我们的方法优于传统的机器学习分类算法和最新的神经网络分类模型。CNN获得本地内容特征；然后，采用多头注意力机制获得RNN模型时间序列的潜在依赖关系。上面提出的神经网络应用于构建的开放专利关系数据集的结果表明，在Precision，Recall和F1方面，我们的方法优于传统的机器学习分类算法和最新的神经网络分类模型。CNN获得本地内容特征；然后，采用多头注意力机制获得RNN模型时间序列的潜在依赖关系。上面提出的神经网络应用于构建的开放专利关系数据集的结果表明，在Precision，Recall和F1方面，我们的方法优于传统的机器学习分类算法和最新的神经网络分类模型。