This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task. This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively. Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared task.

本文介绍了一种基于最短依赖路径和卷积神经网络的方法，用于在科技论文中进行语义关系的提取和分类。该方法在任务中排名第三，对于干净数据和有噪声数据的关系分类整体F1得分分别为76.7和83.2。此外，对于干净数据的综合关系提取和分类，它获得了37.4和33.6的F1得分。

SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks
  with Shortest Dependency Paths for Semantic Relation Extraction and
  Classification in Scientific Papers

SIRIUS-LTG-UiO在SemEval-2018 Task 7中的应用:短依存路径卷积神经网络用于科学论文中的语义关系提取与分类