Named Entity Recognition in Chinese Social Media Base on the Unified Model
YI Li
HUANG Peng
PENG YANBING
CHENG Guang
Abstract：Named Entity Recognition(NER)in Chinese social media is important with the development of the internet.Previ?ous methods focus on in-domain supervised learning which is limited by the rare annotated data.However,there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task.A unified model which can learn from out-of-domain corpora and in-domain unannotated texts is proposed,the unified model contains two major functions,one is for cross-domain learning and the other is for semi-supervised learning.Cross-domain leaning function can learn out-of-domain in?formation based on domain similarity.Semi-Supervised learning function can learn in-domain unannotated information by self-train?ing.Both learning functions outperform existing methods for NER in Chinese social media.Used unified model to experiment get a better result and decrease the workload of manual tagged corpus.
Keywords：named entity recognitionsocial mediacross domain leaningdomain similaritysemi-supervised learningself-training
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2402-2406,2433 )