This paper proposes a Unified Dynamic Relation Tree (DRT) span for tree kernel-based semantic relation extraction between entity names. The basic idea is to apply a variety of lin...
This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For e...
At present, adapting an Information Extraction system to new topics is an expensive and slow process, requiring some knowledge engineering for each new topic. We propose a new par...
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 lo...
Latent Dirichlet allocation is a fully generative statistical language model that has been proven to be successful in capturing both the content and the topics of a corpus of docum...