.png){kind=icon}
返回
Interactive Lexical and Semantic Graphs for Semisupervised Relation Extraction
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2021.3138956.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Semantics
Hidden Markov models
Task analysis
Training data
Data models
Semisupervised learning
Training
Lexical and semantic graphs
prior knowledge
relation extraction (RE)
semisupervised learning
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Mapping past and recent landscape modifications in the Lagoon of Venice through geophysical surveys and historical maps.png)
Anthropocene
AnthropoceneIF0
PREAI
分享收藏分享收藏分享收藏The Source, Spatial Distribution and Risk Assessment of Heavy Metals in Soil from the Pearl River Delta Based on the National Multi-Purpose Regional Geochemical SurveyPLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容