Abstract
While substantial studies have been performed on named entity recognition to date, nested named entity recognition as a research issue has not been well studied, especially for Chinese. In this paper, we take Chinese nested named entity recognition as a cascaded chunking problem on a sequence of words. To approach this problem, we first make a corpus-based investigation of nested structures for Chinese entities and thus propose a dual-layer conditional random fields (CRFs) based solution. To exploit more informative clues for nested named entity recognition, we employ a hybrid chunking scheme to represent the nest structures in Chinese named entities. Moreover, we have also examined the performance of different dual-layer models. Experimental results on different data sets show that the dual-layer CRFs with a hybrid chunk scheme achieve the best performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
