Research on Fine-grained Named-Entity-Recognition Method for Public-Opinion Texts in Northeast Asia
The evolving international situation in Northeast Asia is associated closely with China's development.The construction of a sentiment information knowledge graph for this region enables the effective monitoring of public-opinion hotspots.This not only guides the healthy development of public opinion and assists government decision-making but also prevents political marketing,thus enhancing national language competence and promoting harmonious and stable international relations.Named Entity Recognition(NER)is a key technology and core task in constructing knowledge graphs and has garnered extensive attention from researchers.This study uses real-time hot-sentiment texts related to Northeast Asia from social media and portal websites as data sources.Considering the regional characteristics and geopolitical structure of Northeast Asia,a fine-grained NER dataset comprising 10 major categories and 35 subcategories is established.Furthermore,a sentiment entity-recognition model based on the pretrained language model RoBERTa and a multilayer residual BiLSTM-CRF architecture(RoBERTa-ResBiLSTM-CRF)is proposed.After the model completes label prediction,a post-processing strategy based on rule templates is designed to improve the overall entity-recognition performance.Experimental results demonstrate that the proposed sentiment NER model outperforms the mainstream neural-network models,thus validating the effectiveness of the approach.
fine-grainedNamed Entity Recognition(NER)public opinion textsdeep learningpre-trained language models
国家科技期刊平台
NSTL
万方数据
