已有的跨句多元关系抽取工作将输入文本表示为集成句内和句间依赖关系的复杂文档图,但图中包含的噪声信息会影响关系抽取的效果。针对这种情况,该文利用Graph state LSTM获得上下文信息,再分别利用词级注意力机制或位置感知的注意力机制...已有的跨句多元关系抽取工作将输入文本表示为集成句内和句间依赖关系的复杂文档图,但图中包含的噪声信息会影响关系抽取的效果。针对这种情况,该文利用Graph state LSTM获得上下文信息,再分别利用词级注意力机制或位置感知的注意力机制,自动聚焦在对关系抽取起到决定性作用的关键词上,降低噪声信息的影响。并且比较了两种注意力机制对使用Graph state LSTM进行关系抽取的影响。通过在一个重要的精确医学数据集上进行实验,验证了该文所提出模型的有效性。展开更多
Let H be a handlebody, J={J 1, …, J n} a collection of 2 sided pairwise disjoint simple closed curves on H. The 3 manifold obtained from H by attaching 2 handles to H along the curves in J is called an n re...Let H be a handlebody, J={J 1, …, J n} a collection of 2 sided pairwise disjoint simple closed curves on H. The 3 manifold obtained from H by attaching 2 handles to H along the curves in J is called an n relator 3 manifold, and is denoted as H J. In this paper, a sufficient and necessary condition for H J to be a handlebody is described.展开更多
文摘已有的跨句多元关系抽取工作将输入文本表示为集成句内和句间依赖关系的复杂文档图,但图中包含的噪声信息会影响关系抽取的效果。针对这种情况,该文利用Graph state LSTM获得上下文信息,再分别利用词级注意力机制或位置感知的注意力机制,自动聚焦在对关系抽取起到决定性作用的关键词上,降低噪声信息的影响。并且比较了两种注意力机制对使用Graph state LSTM进行关系抽取的影响。通过在一个重要的精确医学数据集上进行实验,验证了该文所提出模型的有效性。
文摘Let H be a handlebody, J={J 1, …, J n} a collection of 2 sided pairwise disjoint simple closed curves on H. The 3 manifold obtained from H by attaching 2 handles to H along the curves in J is called an n relator 3 manifold, and is denoted as H J. In this paper, a sufficient and necessary condition for H J to be a handlebody is described.
