大连外国语大学

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.

Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocess-ing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsuper-vised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identifi-cation of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in tra-jectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.