<br>FSN：基于过滤分离器网络的联合实体和关系抽取,Entropy

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FSN：基于过滤分离器网络的联合实体和关系抽取
Entropy ( IF 2.1 ) Pub Date : 2024-02-12 , DOI: 10.3390/e26020162
Qicai Dai _{1,

2} , Wenzhong Yang _{1,

2} , Fuyuan Wei _{1,

2} , Liang He _{1,

3} , Yuanyuan Liao ₁

Affiliation

联合实体和关系提取方法由于能够从复杂的文本中提取关系三元组，最近引起了越来越多的关注。然而，现有方法大多数忽略了命名实体识别（NER）子任务特征和关系提取（RE）子任务特征之间的关联性和差异性，导致这两个子任务之间的交互不平衡。为了解决上述问题，我们提出了一种新的联合实体和关系提取方法——FSN。它包含一个过滤分离网络（FSN）模块，利用双向LSTM来过滤和分离句子中包含的信息，并通过拼接操作合并相似的特征，从而解决子任务之间交互不平衡的问题。为了更好地提取每个子任务的局部特征信息，我们采用Transformer中解码器的设计思想和平均池化操作，设计了命名实体识别生成（NERG）模块和关系提取生成（REG）模块，以更好地捕获分别是句子中的实体边界信息和关系三元组中每个关系的实体对边界信息。此外，我们提出了一种动态损失函数，根据每个子任务之间的比例动态调整每个时期每个子任务的学习权重，从而缩小理想结果与现实结果之间的差异。我们在 SciERC 数据集和 ACE2005 数据集上彻底评估了我们的模型。实验结果表明，与基线模型相比，我们的模型取得了令人满意的结果。

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FSN: Joint Entity and Relation Extraction Based on Filter Separator Network

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

更新日期：2024-02-12

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