问题：
提案：We propose a single, joint neural network based model to carry out all the three tasks of boundary identification, entity type classification and relation type classification.
具体做法：“All Word Pairs” model (AWP-NN) as it assigns an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. We also propose to refine output of the AWP-NN model by using inference in Markov Logic Networks (MLN) so that additional domain knowledge can be effectively incorporated
效果：We demonstrate effectiveness of our approach by achieving better end-to-end relation extraction performance than all 4 previous joint modelling approaches, on the standard dataset of ACE 2004.
背景：End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions.
额，等等，这个针对RE的end-to-end的意思是指NER被当做RE的一部分是很自然的事情啊。
In contrast, end-to-end relation extraction deals with plain sentences without assuming any knowledge of entity mentions in them. The task of end-to-end relation extraction consists of three sub-tasks: i) identifying boundaries of entity mentions, ii) identifying entity types of these mentions and iii) identifying appropriate semantic relation for each pair of mentions.
end-to-end relation extraction可以分为三个子任务：
识别entity的boundaries
识别entity的types
识别entity之间的relation
通常来说，这三个task是pipeline方式的。这样会导致误差传递。这篇文章将Neural Networks and Markov Logic Networks进行联合，通过end-to-end的方式来三个子任务。
贡献：
modelling boundary detection problem by introducing a special relation type WEM
a single, joint neural network model for all three subtasks of end-to-end relation extraction
一句话总结：
通过将NN和MLN进行联合训练，做到end-to-end RE。
资源：
论文信息：
笔记：
额，等等，这个针对RE的end-to-end的意思是指NER被当做RE的一部分是很自然的事情啊。
In contrast, end-to-end relation extraction deals with plain sentences without assuming any knowledge of entity mentions in them. The task of end-to-end relation extraction consists of three sub-tasks: i) identifying boundaries of entity mentions, ii) identifying entity types of these mentions and iii) identifying appropriate semantic relation for each pair of mentions.
end-to-end relation extraction可以分为三个子任务：
通常来说，这三个task是pipeline方式的。这样会导致误差传递。这篇文章将Neural Networks and Markov Logic Networks进行联合，通过end-to-end的方式来三个子任务。
贡献：
模型图：
结果：
接下来要看的论文：