论文标题
从物理世界中采矿常识性事实
Mining Commonsense Facts from the Physical World
论文作者
论文摘要
物理世界的文本描述隐含地提及常识性事实，而常识知识基础明确表示诸如三元组之类的事实。与大幅增加的文本数据相比，现有知识库的覆盖范围远离完成。关于填充知识基础的大多数先前研究主要集中在壁炉底座上。自动完成常识性知识库以改善其覆盖范围的探索不足。在本文中，我们提出了一项新的任务，即从描述物理世界的原始文本中挖掘常识性事实。我们建立了一个有效的新模型，该模型融合了序列文本和现有知识库资源的信息。然后，我们创建两个大型注释数据集，每个数据集都有大约200K实例，以完成常识知识基础的完成。经验结果表明，我们的模型明显胜过基准。
Textual descriptions of the physical world implicitly mention commonsense facts, while the commonsense knowledge bases explicitly represent such facts as triples. Compared to dramatically increased text data, the coverage of existing knowledge bases is far away from completion. Most of the prior studies on populating knowledge bases mainly focus on Freebase. To automatically complete commonsense knowledge bases to improve their coverage is under-explored. In this paper, we propose a new task of mining commonsense facts from the raw text that describes the physical world. We build an effective new model that fuses information from both sequence text and existing knowledge base resource. Then we create two large annotated datasets each with approximate 200k instances for commonsense knowledge base completion. Empirical results demonstrate that our model significantly outperforms baselines.
