Semi-supervised Relation Extraction Research Articles

Adversarial Multi-Teacher Distillation for Semi-Supervised Relation Extraction.

The shortage of labeled data has been a long-standing challenge for relation extraction (RE) tasks. Semi-supervised RE (SSRE) is a promising way through annotating unlabeled samples with pseudolabels as additional training data. However, some pseudolabels on unlabeled data might be erroneous and will bring misleading knowledge into SSRE models. For this reason, we propose a novel adversarial multi-teacher distillation (AMTD) framework, which includes multi-teacher knowledge distillation and adversarial training (AT), to capture the knowledge on unlabeled data in a refined way. Specifically, we first develop a general knowledge distillation (KD) technique to learn not only from pseudolabels but also from the class distribution of predictions by different models in existing SSRE methods. To improve the robustness of the model, we further empower the distillation process with a language model-based AT technique. Extensive experimental results on two public datasets demonstrate that our framework significantly promotes the performance of the base SSRE methods.

Interactive Lexical and Semantic Graphs for Semisupervised Relation Extraction.

The performance of relation extraction (RE) is hindered by the lack of sufficient labeled data. Semisupervised methods can offer to help hands with this problem by augmenting high-quality unlabeled samples into the training data. However, existing semisupervised RE methods either need a set of manually defined rules or rely on the classifier trained on the small labeled data, i.e., the former requires the heavy intervention of human knowledge, and the latter is bound to the number and the quality of the labeled data. In this article, we present a novel semisupervised RE method that involves small human efforts and is robust to the size of the initial set of labeled data. Specifically, we adopt only two simple rules to build the lexical and semantic graphs which connect the labeled samples with the unlabeled ones. In this way, the graphs are much easier to construct yet keep the ability to transfer knowledge from labeled samples to unlabeled ones. We then develop a graph interaction module to fully exploit the reference information in lexical and semantic graphs, which is used to jointly recognize the high-quality unlabeled samples with the classifier. We conduct extensive experimental results on two public datasets. The results demonstrate that our proposed method significantly outperforms the state-of-the-art baselines.

Competition or Cooperation? Exploring Unlabeled Data via Challenging Minimax Game for Semi-supervised Relation Extraction

Competition or Cooperation? Exploring Unlabeled Data via Challenging Minimax Game for Semi-supervised Relation Extraction

Support Vector Machine with Ensemble Tree Kernel for Relation Extraction.

Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall, F-measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others.

Graph-Based Semi-Supervised Relation Extraction

PDF HTML阅读 XML下载 导出引用 引用提醒 基于图的半监督关系抽取 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant Nos.60803078, 60773011 (国家自然科学基金) Graph-Based Semi-Supervised Relation Extraction Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:提出利用基于图的半监督学习算法,即标注传递算法,指导计算机从非结构化的文本中自动识别出实体之间的关系.该方法首先利用图策略来建立关系抽取的模型.在这个图模型中,各个有标签和未标签的样本被表示成图上的各个节点,而样本间的距离则作为图上各边的权重.然后,关系抽取的任务就转化成在这个图上估计出一个满足全局一致性假设的标注函数.通过对ACE(automatic content extraction)语料库的评测,结果显示,当只有少量的标签样本时,采用该标注传递的方法可以获得比基于SVM(support vector machine)的有监督关系抽取更好的性能,同时也明显优于基于Bootstrapping的半监督关系抽取的方法. Abstract:This paper investigates a graph-based semi-supervised learning algorithm, that is, label propagation algorithm for relation extraction. Labeled and unlabeled examples are represented as the nodes, and their distances as the weights of edges in the graph. The relation extraction tries to obtain a labeling function on this graph to satisfy the global consistency assumption. Experimental results on the ACE (automatic content extraction) corpus showed that this method achieves a better performance than SVM (support vector machine) when only very few labeled examples are available, and it also performs better than bootstrapping for the relation extraction task. 参考文献 相似文献 引证文献