Neural Machine Translation(NMT)based system is an important technology for translation applications.However,there is plenty of rooms for the improvement of NMT.In the process of NMT,traditional word vector cannot dist...Neural Machine Translation(NMT)based system is an important technology for translation applications.However,there is plenty of rooms for the improvement of NMT.In the process of NMT,traditional word vector cannot distinguish the same words under different parts of speech(POS).Aiming to alleviate this problem,this paper proposed a new word vector training method based on POS feature.It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors.In the experiments,we conducted extensive experiments to evaluate our methods.The experimental result shows that the proposed method is beneficial to improve the quality of translation from English into Chinese.展开更多
Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine translation.However,traditional methods based...Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine translation.However,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application.In order to solve this problem,we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance.In this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition.Then we design a decoder to simplify the decoding process.Experiments show that the proposed method can effectively improve the accuracy of translation.展开更多
Gene therapy holds great promise for curing cancer by editing the deleterious genes of tumor cells,but the lack of vector systems for efficient delivery of genetic material into specific tumor sites in vivo has limite...Gene therapy holds great promise for curing cancer by editing the deleterious genes of tumor cells,but the lack of vector systems for efficient delivery of genetic material into specific tumor sites in vivo has limited its full therapeutic potential in cancer gene therapy.Over the past two decades,increasing studies have shown that lentiviral vectors(LVs)modified with different glycoproteins from a donating virus,a process referred to as pseudotyping,have altered tropism and display cell-type specificity in transduction,leading to selective tumor cell killing.This feature of LVs together with their ability to enable high efficient gene delivery in dividing and non-dividing mammalian cells in vivo make them to be attractive tools in future cancer gene therapy.This review is intended to summarize the status quo of some typical pseudotypings of LVs and their applications in basic anti-cancer studies across many malignancies.The opportunities of translating pseudotyped LVs into clinic use in cancer therapy have also been discussed.展开更多
基金This work is supported by the National Natural Science Foundation of China(61872231,61701297).
文摘Neural Machine Translation(NMT)based system is an important technology for translation applications.However,there is plenty of rooms for the improvement of NMT.In the process of NMT,traditional word vector cannot distinguish the same words under different parts of speech(POS).Aiming to alleviate this problem,this paper proposed a new word vector training method based on POS feature.It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors.In the experiments,we conducted extensive experiments to evaluate our methods.The experimental result shows that the proposed method is beneficial to improve the quality of translation from English into Chinese.
基金supported by National Social Science Fund of China(Youth Program):“A Study of Acceptability of Chinese Government Public Signs in the New Era and the Countermeasures of the English Translation”(No.:13CYY010)the Subject Construction and Management Project of Zhejiang Gongshang University:“Research on the Organic Integration Path of Constructing Ideological and Political Training and Design of Mixed Teaching Platform during Epidemic Period”(No.:XKJS2020007)Ministry of Education IndustryUniversity Cooperative Education Program:“Research on the Construction of Cross-border Logistics Marketing Bilingual Course Integration”(NO.:202102494002).
文摘Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine translation.However,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application.In order to solve this problem,we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance.In this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition.Then we design a decoder to simplify the decoding process.Experiments show that the proposed method can effectively improve the accuracy of translation.
基金supported by the National Natural Science Foundation of China(No.81872071)the Fundamental Research Funds for the Central Universities(China)(No.SWU120054)+1 种基金the Natural Science Foundation of Chongqing(China)(No.cstc2019jcyj-zdxmX0033)the Fundamental Research Funds for the Central Universities(China)(No.XYDS201912).
文摘Gene therapy holds great promise for curing cancer by editing the deleterious genes of tumor cells,but the lack of vector systems for efficient delivery of genetic material into specific tumor sites in vivo has limited its full therapeutic potential in cancer gene therapy.Over the past two decades,increasing studies have shown that lentiviral vectors(LVs)modified with different glycoproteins from a donating virus,a process referred to as pseudotyping,have altered tropism and display cell-type specificity in transduction,leading to selective tumor cell killing.This feature of LVs together with their ability to enable high efficient gene delivery in dividing and non-dividing mammalian cells in vivo make them to be attractive tools in future cancer gene therapy.This review is intended to summarize the status quo of some typical pseudotypings of LVs and their applications in basic anti-cancer studies across many malignancies.The opportunities of translating pseudotyped LVs into clinic use in cancer therapy have also been discussed.
