基于指针网络的实体与关系联合抽取方法

针对现有实体和关系联合抽取方法中存在的实体与关系依赖建模不足、实体发生重叠难以抽取其所涉及的多个关系的问题,设计了基于深度学习的联合抽取框架。首先针对依赖建模不足问题,从预训练语料中提取实体共现特征,建模了实体间的潜在语义关系和实体与关系之间的依赖关系。其次提出了新颖的指针标注方法,该标注方法可以通过指针表示关系类别,由于任一实体可以被多个指针指向,所以可以在一段文本中标注重叠的实体并抽取多个实体—关系三元组结果。最后,为了有效利用单词的丰富语义和指针之间依赖的信息,设计了一个标签感知注意力机制,融合了包括来自编码层的字词信息、相关的共现语义信息。与研究中前沿的联合提取方法相比,该方法在百度DuIE测试集上实现了F₁值的增加。通过实验结果表明指针标注方法在一定程度上可以解决实体重叠问题。

Joint extraction method of entity and relationship based on pointer network

In order to solve the problems of insufficient modeling of entity and relationship dependence and the difficulty of extracting multiple relationships involved in existing joint extraction methods of entities and relationships,this paper designed a joint extraction framework based on deep learning.Firstly,for the problem of insufficient dependency modeling,the framework extracted entity co-occurrence features from the pre-trained corpus,and modeled the potential semantic relationship between entities and the dependency relationship between entities and relationships.Secondly,it included a novel pointer labeling method.This labeling method could represent the relationship category through a pointer.Since any entity could be pointed by multiple pointers,it was possible to mark overlapping entities in a piece of text and extracted multiple entity-relation triplets result.Finally,in order to effectively use the rich semantics of words and the information dependent on pointers,it designed a tag-aware attention mechanism was necessary,which incorporated word information from the coding layer and related co-occurrence semantic information.Compared with the joint extraction method at the forefront of research,the proposed method achieved an increase in F₁value on the Baidu DuIE test set.The experimental results show that the pointer labeling method can solve the problem of entity overlap to a certain extent.