Extracting the relations that exist between two entities is an important step in numerous Web-related tasks such as information extraction. A supervised relation extraction system that is trained to extract a particular relation type might not accurately extract a new type of a relation for which it has not been trained. However, it is costly to create training data manually for every new relation type that one might want to extract. We propose a method to adapt an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F -score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.
|ホスト出版物のタイトル||IJCAI International Joint Conference on Artificial Intelligence|
|出版ステータス||Published - 2011|
|イベント||22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia|
継続期間: 2011 7月 16 → 2011 7月 22
|Other||22nd International Joint Conference on Artificial Intelligence, IJCAI 2011|
|Period||11/7/16 → 11/7/22|
ASJC Scopus subject areas