Set in the context of pre-Qin period,Mengzi's contribution to the discussions of language issues connects later debates over name and reality to Kongzi's idea of zhengming.Inheriting Kongzi's socio-politic...Set in the context of pre-Qin period,Mengzi's contribution to the discussions of language issues connects later debates over name and reality to Kongzi's idea of zhengming.Inheriting Kongzi's socio-political concern,Mengzi disclosed the ambiguity and contradictions latent in contemporary philosophical discourse through his argumentation.In response to Mengzi,Gongsun Long and later Moists developed the logico-linguistic strain implied in Mengzi's discussions,but diverged from each other in two oppositional veins.While Gongsun Long attempted to defend Mengzi's project of rectifying reality in terms of the correct use of names,the later Moists proposed the opposite,denying the possibility to use language as the standard to rectify reality.Combining the pragmatism of later Moists with Zhuangzi's antilanguage position,Xunzi renounced the logico-linguistic approach and prioritized tradition and common sense over logical and linguistic standards of right and wrong.展开更多
Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate ...Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.展开更多
Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and condi...Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.展开更多
文摘Set in the context of pre-Qin period,Mengzi's contribution to the discussions of language issues connects later debates over name and reality to Kongzi's idea of zhengming.Inheriting Kongzi's socio-political concern,Mengzi disclosed the ambiguity and contradictions latent in contemporary philosophical discourse through his argumentation.In response to Mengzi,Gongsun Long and later Moists developed the logico-linguistic strain implied in Mengzi's discussions,but diverged from each other in two oppositional veins.While Gongsun Long attempted to defend Mengzi's project of rectifying reality in terms of the correct use of names,the later Moists proposed the opposite,denying the possibility to use language as the standard to rectify reality.Combining the pragmatism of later Moists with Zhuangzi's antilanguage position,Xunzi renounced the logico-linguistic approach and prioritized tradition and common sense over logical and linguistic standards of right and wrong.
文摘Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.
基金National Natural Science Foundations of China (No.60873179, No.60803078)
文摘Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.
