Relation Adaptation: Learning to Extract Novel Relations with Minimum Supervision
Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka
The World Wide Web includes semantic relations of numerous types that exist among different entities. Extracting the relations that exist between two entities is an important step in various Web-related tasks such as information retrieval, information extraction, and social network extraction. A supervised relation extraction system that is trained to extract a particular relation type (source relation) might not accurately extract a new type of a relation (target 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 \textit{adapt} an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: \textit{learning a lower-dimensional projection} between different relations, and \textit{learning a relational classifier} for the target relation type with instance sampling. First, to represent a semantic relation that exist between two entities, we extract lexical and syntactic patterns from contexts in which those two entities co-occur. Then, we construct a bipartite graph between relation-specific and relation-independent patterns. Spectral clustering is performed on the bipartite graph to compute a lower-dimensional projection. Second, we train a classifier for the target relation type using a small number of labeled instances. To account for the lack of target relation training instances, we present a one-sided under-sampling method. 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.