论文标题
建模用于关系提取的多粒性分层特征
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
论文作者
论文摘要
关系提取是自然语言处理（NLP）的关键任务，该任务旨在从给定文本中提取实体对之间的关系。最近，随着深度神经网络的发展，关系提取（RE）取得了显着的进步。大多数现有的研究重点是使用外部知识（例如知识图和依赖树树）构建明确的结构化特征。在本文中，我们提出了一种新的方法，以提取仅基于原始输入句子的多晶格特征。我们表明，即使没有外部知识，也可以实现有效的结构化特征。基于输入句子的三种功能是完全利用的，在实体中提及级别，段级别和句子级别。这三个都是共同和分层建模的。我们在三个公共基准上评估了我们的方法：Semeval 2010 Task 8，Tacred和Tacred重新审视。为了验证有效性，我们将方法应用于LSTM和BERT等不同编码器。实验结果表明，我们的方法显着优于甚至使用外部知识的现有最新模型。广泛的分析表明，我们的模型的性能是由捕获多晶格特征及其层次结构的模型的贡献。代码和数据可在\ url {https://github.com/xnliang98/sms}中获得。
Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) has achieved remarkable progress with the development of deep neural networks. Most existing research focuses on constructing explicit structured features using external knowledge such as knowledge graph and dependency tree. In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences. We show that effective structured features can be attained even without external knowledge. Three kinds of features based on the input sentences are fully exploited, which are in entity mention level, segment level, and sentence level. All the three are jointly and hierarchically modeled. We evaluate our method on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred Revisited. To verify the effectiveness, we apply our method to different encoders such as LSTM and BERT. Experimental results show that our method significantly outperforms existing state-of-the-art models that even use external knowledge. Extensive analyses demonstrate that the performance of our model is contributed by the capture of multi-granularity features and the model of their hierarchical structure. Code and data are available at \url{https://github.com/xnliang98/sms}.
