- author: Yan Fan, Chengyu Wang, Xiaofeng He
- abstract: The state-of-the-art methods for relation classification are primarily based on deep neural net- works. This kind of supervised learning method suffers from not only limited training data, but also the large number of low-frequency relations in specific domains. In this paper, we propose the task of exploratory relation classification for domain knowledge harvesting. The goal is to learn a classifier on pre-defined relations and discover new relations expressed in texts. A dynamically structured neural network is introduced to classify entity pairs to a continuously expanded relation set. We further propose the similarity sensitive Chinese restaurant process to discover new relations. Experiments conducted on a large corpus show the effectiveness of our neural network, while new relations are discovered with high precision and recall.
- keywords: relation classification
- interpretation: 来源: 暂无
- pdf: link
- code:
- dataset: Wikipedia
- ppt/video:
- curation: Jiong Zhang