Semantic Relation Extraction Based on Semi-supervised Learning

Many tasks of information extraction or natural language processing have a property that the data naturally consist of several views disjoint subsets of features. Specifically, a semantic relationship can be represented with some entity pairs or contexts surrounding the entity pairs. For example, the Person-Birthplace relation can be recognized from the entity pair view, such as (Albert Einstein, Ulm), (Pablo Picasso, Malaga) and so on. On the other side, this relation can be identified with some contexts, such as "A was born in B", "B, the birth place of A" and so on. To leverage the unlabeled data in the training stage, semi-supervised learning has been applied to relation extraction task. In this paper, we propose a multi-view semi-supervised learning algorithm, Co-Label Propagation, to combine the 'information' from both the entity pair view and the context view. In propagation process, the label scores of classes are spread not only in the entity pair view and the context view, but also between the two views. The proposed algorithm is evaluated using semantic relation classification tasks. The experiment results validate its effectiveness.