Active learning based named entity recognition and its application in natural language coverless information hiding

Named entity recognition systems trained on one domain usually have a substantial drop in performance when applied to a different domain. In this paper, we apply active learning to domain adaptation for named entity recognition systems, propose various sampling optimizations, and show that the labeling effort can be reduced by over 92% while achieving the same performance as supervised method. Named entity recognition can be effectively applied to information extraction, machine translation, text classification and many other areas. We propose a new application area for named entity recognition, namely in natural language information hiding: a novel coverless information hiding method based on text big data is proposed, utilizing named entities to mark the locations of the hidden information. Coverless information hiding is a brand new area of information hiding that achieves the transmission of hidden information without any modification in the carrier text. Furthermore, active learning allows our information hiding method to be applied to text from new domains without substantial labeling effort.