Research on Weakly-Supervised Entity Relation Extraction of Specific Domain Based on Entropy Minimization

Abstract

There are two major issues of automatic entity relation extraction: human intervention and difficulty in labeling corpus. For these two problems, combined with the characteristics of the tourism domain, this paper adopts a weakly-supervised extraction method of entity relation based on entropy minimization. This method firstly extracts the characteristic words by the idea of scalar clustering with small-scale stratified marked instances, and constructs the initial classifier with maximum entropy machine learning algorithm. Then use the initial classifier of certain accuracy to classify the unlabeled instances, and add the instances of the minimum information entropy to the training corpus set to continually expand the scale of training data set. Finally, repeat the above iterative process until the performance of classifier is to be stabilized, and then a final extraction classifier of entity relation in specific domain will be constructed. Experiments performed on the corpus of tourism domain show that, not only can this method reduce the dependence of entity relation extraction on manual intervention, but it could effectively improve the performance of entity relation extraction, the F value of which is up to 63.69 %.