In order to deal with partical occlusion, a semantic interaction based moving human tracking approach is put forward. Firstly human is modeled as moving blobs which are described as blob descriptions. Then moving blob...In order to deal with partical occlusion, a semantic interaction based moving human tracking approach is put forward. Firstly human is modeled as moving blobs which are described as blob descriptions. Then moving blobs are updated and verified by projecting these descriptions. The approach exploits improved fast gauss transform and chooses source and target samples to reduce compute cost. Multi-moving human can be tracked simply and part occlusion can be done well.展开更多
In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple e...In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.展开更多
Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstruc...Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstructured text,i.e.,entities and the relations between them.At present,the main dilemma of Chinese entity relation extraction research lies in nested entities,relation overlap,and lack of entity relation interaction.This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge,imprecise syntactic structure,and lack of semantic roles.To address these challenges,this paper presents an innovative“character-level”Chinese part-of-speech(CN-POS)tagging approach and incorporates part-of-speech(POS)information into the pre-trained model,aiming to improve its semantic understanding and syntactic information processing capabilities.Additionally,A relation reference filling mechanism(RF)is proposed to enhance the semantic interaction between relations and entities,utilize relations to guide entity modeling,improve the boundary prediction ability of entity models for nested entity phenomena,and increase the cascading accuracy of entity-relation triples.Meanwhile,the“Queue”sub-task connection strategy is adopted to alleviate triplet cascading errors caused by overlapping relations,and a Syntax-enhanced entity relation extraction model(SE-RE)is constructed.The model showed excellent performance on the self-constructed E-commerce Product Information dataset(EPI)in this article.The results demonstrate that integrating POS enhancement into the pre-trained encoding model significantly boosts the performance of entity relation extraction models compared to baseline methods.Specifically,the F1-score fluctuation in subtasks caused by error accumulation was reduced by 3.21%,while the F1-score for entity-relation triplet extraction improved by 1.91%.展开更多
文摘In order to deal with partical occlusion, a semantic interaction based moving human tracking approach is put forward. Firstly human is modeled as moving blobs which are described as blob descriptions. Then moving blobs are updated and verified by projecting these descriptions. The approach exploits improved fast gauss transform and chooses source and target samples to reduce compute cost. Multi-moving human can be tracked simply and part occlusion can be done well.
基金Science and Technology Innovation 2030-Major Project of“New Generation Artificial Intelligence”granted by Ministry of Science and Technology,Grant Number 2020AAA0109300.
文摘In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.
基金funded by the National Key Technology R&D Program of China under Grant No.2021YFD2100605the National Natural Science Foundation of China under Grant No.62433002+1 种基金the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions under Grant No.BPHR20220104Beijing Scholars Program under Grant No.099.
文摘Entity relation extraction,a fundamental and essential task in natural language processing(NLP),has garnered significant attention over an extended period.,aiming to extract the core of semantic knowledge from unstructured text,i.e.,entities and the relations between them.At present,the main dilemma of Chinese entity relation extraction research lies in nested entities,relation overlap,and lack of entity relation interaction.This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge,imprecise syntactic structure,and lack of semantic roles.To address these challenges,this paper presents an innovative“character-level”Chinese part-of-speech(CN-POS)tagging approach and incorporates part-of-speech(POS)information into the pre-trained model,aiming to improve its semantic understanding and syntactic information processing capabilities.Additionally,A relation reference filling mechanism(RF)is proposed to enhance the semantic interaction between relations and entities,utilize relations to guide entity modeling,improve the boundary prediction ability of entity models for nested entity phenomena,and increase the cascading accuracy of entity-relation triples.Meanwhile,the“Queue”sub-task connection strategy is adopted to alleviate triplet cascading errors caused by overlapping relations,and a Syntax-enhanced entity relation extraction model(SE-RE)is constructed.The model showed excellent performance on the self-constructed E-commerce Product Information dataset(EPI)in this article.The results demonstrate that integrating POS enhancement into the pre-trained encoding model significantly boosts the performance of entity relation extraction models compared to baseline methods.Specifically,the F1-score fluctuation in subtasks caused by error accumulation was reduced by 3.21%,while the F1-score for entity-relation triplet extraction improved by 1.91%.
