Novel coronavirus,now named COVID-19,has swept the world,which is regarded as‘public enemy number one’by WHO.In these months,the coronavirus has become a hot topic and led various public opinion.The traditional stra...Novel coronavirus,now named COVID-19,has swept the world,which is regarded as‘public enemy number one’by WHO.In these months,the coronavirus has become a hot topic and led various public opinion.The traditional strategies for public opinion analyzing seldom take the entities and behaviors into consideration.Focusing on the high fluctuation of public opinion of novel coronavirus event,we propose a Key-Information-oriented Convolutional Neural Network(KIN-CNN)to analyze both relevant entities and behaviors in addition to public opinion trend on Chinese corpus.Firstly,we establish a knowledge set according to the characteristic of distribution in corpus of emotions,behaviors and entities.Secondly,we integrate the other prior knowledge to initialize the convolution kernel for better model performance.Thirdly,as COVID-19 event develops,the dominant public opinion trend is obtained by our approach.Furthermore,the relationship of dominant public opinion with entities and behaviors is established as well in this research.展开更多
Entity and relation extraction is an indispensable part of domain knowledge graph construction,which can serve relevant knowledge needs in a specific domain,such as providing support for product research,sales,risk co...Entity and relation extraction is an indispensable part of domain knowledge graph construction,which can serve relevant knowledge needs in a specific domain,such as providing support for product research,sales,risk control,and domain hotspot analysis.The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets.However,the performance of these methods degrades when they face domain-specific datasets.Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains.Relation extraction is based on the hypothesis that the relations hidden in sentences are unified,thereby neglecting that relations may be diverse in different entity tuples.To address the problems above,this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’dependence.Second,domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction.Finally,experiments were designed to verify the effectiveness of our proposed methods.Experimental results on two domains,including laser industry and unmanned ship,showed the superiority of our methods.The F1 value on laser industry entity,unmanned ship entity,laser industry relation,and unmanned ship relation datasets is improved by+1%,+6%,+2%,and+1%,respectively.In addition,the extraction accuracy of entity relation triplet reaches 83%and 76%on laser industry entity pair and unmanned ship entity pair datasets,respectively.展开更多
文摘Novel coronavirus,now named COVID-19,has swept the world,which is regarded as‘public enemy number one’by WHO.In these months,the coronavirus has become a hot topic and led various public opinion.The traditional strategies for public opinion analyzing seldom take the entities and behaviors into consideration.Focusing on the high fluctuation of public opinion of novel coronavirus event,we propose a Key-Information-oriented Convolutional Neural Network(KIN-CNN)to analyze both relevant entities and behaviors in addition to public opinion trend on Chinese corpus.Firstly,we establish a knowledge set according to the characteristic of distribution in corpus of emotions,behaviors and entities.Secondly,we integrate the other prior knowledge to initialize the convolution kernel for better model performance.Thirdly,as COVID-19 event develops,the dominant public opinion trend is obtained by our approach.Furthermore,the relationship of dominant public opinion with entities and behaviors is established as well in this research.
基金This work is funded by the Shanghai Sailing Program(Grant No.20YF1413800)Military Medical Science and Technology Youth Cultivating Program(Grant No.20QNPY106)High Performance Computing Center of Shanghai University,and Shanghai Engineering Research Center of Intelligent Computing System(Grant No.19DZ2252600).
文摘Entity and relation extraction is an indispensable part of domain knowledge graph construction,which can serve relevant knowledge needs in a specific domain,such as providing support for product research,sales,risk control,and domain hotspot analysis.The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets.However,the performance of these methods degrades when they face domain-specific datasets.Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains.Relation extraction is based on the hypothesis that the relations hidden in sentences are unified,thereby neglecting that relations may be diverse in different entity tuples.To address the problems above,this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’dependence.Second,domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction.Finally,experiments were designed to verify the effectiveness of our proposed methods.Experimental results on two domains,including laser industry and unmanned ship,showed the superiority of our methods.The F1 value on laser industry entity,unmanned ship entity,laser industry relation,and unmanned ship relation datasets is improved by+1%,+6%,+2%,and+1%,respectively.In addition,the extraction accuracy of entity relation triplet reaches 83%and 76%on laser industry entity pair and unmanned ship entity pair datasets,respectively.
