Abstract
POS tagging is a fundamental work in Natural Language Processing, which determines the subsequent processing quality, and the ambiguity of multi-category words directly affects the accuracy of Vietnamese POS tagging. At present, the POS tagging of English and Chinese has achieved better results, but the accuracy of Vietnamese POS tagging is still to be improved. For address this problem, this paper proposes a novel method of Vietnamese POS tagging based on multi-category words disambiguation model and Part of Speech dictionary, the multi-category words dictionary and the non-multi-category words dictionary are generated from the Vietnamese dictionary, which are used to build POS tagging corpus. 396,946 multi-category words have been extracted from the corpus, by using statistical method, the maximum entropy disambiguation model of Vietnamese part of speech is constructed, based on it, the multi-category words and the non-multi-category words are tagged. Experimental results show that the method proposed in the paper is higher than the existing model, which is proved that the method is feasible and effective.
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.
