Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval o...
This paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. I...
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu ...
This paper proposes a supervised learning method for detecting a semantic relation between a given pair of named entities, which may be located in different sentences. The method ...
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...