Sciweavers

Share
AIRS
2010
Springer

Semantic Relation Extraction Based on Semi-supervised Learning

9 years 19 days ago
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 PersonBirthplace 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 multiview 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 ...
Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where AIRS
Authors Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka
Comments (0)
books