Semi-supervised Learning for Vietnamese Named Entity Recognition using Online Conditional Random Fields

Abstract

We present preliminary results for the named entity recognition problem in the Vietnamese language. For this task, we build a system based on conditional random fields and address one of its challenges: how to combine labeled and unlabeled data to create a stronger system. We propose a set of features that is useful for the task and conduct experiments with different settings to show that using bootstrapping with an online learning algorithm called Margin Infused Relaxed Algorithm increases the performance of the models. c 2015 Association for Computational Linguistics.