Bootstrapping and rule-based model for recognizing vietnamese named entity

Abstract

This paper intends to address and solve the problem Vietnamese Named Entity recognition and classification (VNER) by using the bootstrapping algorithm and rule-based model. The rule-based model relies on contextual rules to provide contextual evidence that a VNE belongs to a category. These rules exploit linguistic constraints of category are constructed by using the bootstrapping algorithm. Bootstrapping algorithm starts with a handful of seed VNEs of a given category and accumulate all contextual rules found around these seeds in a large corpus. These rules are ranked and used to find new VNEs. Our experimented corpus is generated from about 250.034 online news articles and over 9.000 literatures. Our VNER system consists 27 categories and more 300.000 VNEs which are recognized and categorized. The accuracy of the recognizing and classifying algorithm is about 95%. © 2014 Springer International Publishing Switzerland.