Abstract:

To solve the limitation by the scale of labeled corpus of the supervised learning method, a semi-supervised method based on information entropy was proposed to extract entity relation using small-scale training data. Firstly, combined with field vocabulary to select small-scale training data, an initial maximum entropy classifier of certain accuracy was constructed to predict some new candidate instances from unlabeled data. Secondly, applied the method of information entropy by setting different entropy value and cycling many times,some new instances of the higher credibility from candidate instances were selected to expand the training data. Finally, the training classifier was re-iteratived with the expanded training data until classifier performance  tended to stable iteration termination, which achieved field entity relation extraction. Experimental results showed that the semi-supervised learning method based on information entropy achieved better learning results compared to other methods.
 

Key words: information entropy, semi-supervised, the maximum entropy classifier, unlabeled, credibility