In this paper,the geographic name in Southwest China is regarded as a symbolic representation of human beings,and the dynamic social and historical process behind the place names is restored from the perspective of th...In this paper,the geographic name in Southwest China is regarded as a symbolic representation of human beings,and the dynamic social and historical process behind the place names is restored from the perspective of the symbolic anthropology.There are three paths in the construction and evolution of geographic names in Southwest China—Ethnic information,sacred systems,and local representation,which have been rewritten,masked,and reconstructed over the years.As a result,the system of geographical names is gradually formed and integrated into local memory through space building,culture filling,and so on,affecting and influencing local group identity and cognitive concept.展开更多
Research on the next generation network architecture is a hot topic. To meet the requirements of the new Internet environment and eliminate the shortcomings of the existing network, integrated network is presented. In...Research on the next generation network architecture is a hot topic. To meet the requirements of the new Internet environment and eliminate the shortcomings of the existing network, integrated network is presented. In the naming system part, a system based on Chord algorithm was used, and multi-path is introduced to improve the name resolution reliability. In this paper, we mainly pay attention to the reliability model of integrated naming network system which can be attributed as a multi-path transmission issue, and the name resolution paths used in the network path may be cut off by attacks or other events. This paper focuses on parallel multi-path, which is recoverable when failure happens, transmission reliability, and proposes a corresponding reliability model to get the probability of successful transmission in such conditions. Finally, a numerical simulation is devised to demonstrate the multi-path name resolution's high reliability.展开更多
Academia has recently proposed new naming systems based on flat Distributed Hash Table （DHT）. These naming systems are designed to overcome defects--such as lack of support for data migration and replication--in the...Academia has recently proposed new naming systems based on flat Distributed Hash Table （DHT）. These naming systems are designed to overcome defects--such as lack of support for data migration and replication--in the Domain Name System （DNS）. DHT naming systems have long resolution delay and are not suitable for practical application. This paper introduces two new naming systems that have the advantages of both DNS and DHT systems. The first is a three-layer system based on one-hop DHT and is suitable for small-scale application. The second adopts a hybrid DHT structure, can be implemented in different domains, and can be applied globally. Theoretical analyses demonstrate that these two systems are feasible for practical use.展开更多
Traditional taxonomic sorting of samples into recognizable taxonomic units, such as morphospecies or morphotypes, is commonly relied upon in conservation biology and ethnobiological studies. However, understanding the...Traditional taxonomic sorting of samples into recognizable taxonomic units, such as morphospecies or morphotypes, is commonly relied upon in conservation biology and ethnobiological studies. However, understanding the criteria used for traditional nomenclature of fungi, particularly wild edible mushrooms across linguistic groups, remains limited, leading to frequent errors in species recognition. This study seeks to assess how linguistic affiliations influence the local naming of useful wild mushrooms, and is the first of its kind in Benin. In order to understand how local people recognize, classify and name mushrooms that develop in or close to their villages, 2234 respondents from five socio-linguistic groups across three geographical areas were interviewed. Structured and semi-structured interviews were conducted to gather data on the local naming criteria for edible wild mushrooms. Citation scores were recorded for both nomenclature criteria and species, considering variables such as linguistic groups, age, and language. Twenty-two nomenclature criteria were used by local people to name edible wild species. Strong similarity in classification and naming of species was shown in 97% of the languages, while 3% showed differing classification criteria. The Gur, Atlantic, and Mande linguistic groups demonstrated more comprehensive traditional taxonomic and nomenclatural knowledge, sharing six common criteria: texture, taste, size, kingdom (Fungi), form, and substrate. Overall, local populations possess extensive knowledge regarding the diversity of wild edible mushrooms in their environment.展开更多
The naming of rock hand specimens is usually conducted by geological workers based on observed mineral composition,texture characteristics,etc.,combined with their own knowledge reserves.The accuracy of identification...The naming of rock hand specimens is usually conducted by geological workers based on observed mineral composition,texture characteristics,etc.,combined with their own knowledge reserves.The accuracy of identification results is limited by the experience,research interests,and identification level of the identifier,as well as the complexity of the rock composition.To improve the efficiency of rock hand specimen identification,this paper proposes a method for rock image recognition and classification based on deep learning and the Inception-v3 model.It encompasses the preprocessing of collected photographs of typical intrusive rock hand specimens,along with augmenting the sample size through data augmentation methods,culminating in a comprehensive dataset comprising 12501 samples.Experimental results show that the model has good learning ability when there is sufficient data.Through iterative training of the Inception-v3 model on the rock dataset,the accuracy of rock image recognition reaches 92.83%,with a loss of only 0.2156.Currently,several common types of intrusive rocks can be identified:gabbro,granite,diorite,peridotite,granodiorite,diabase,and granite porphyry.Software is developed for open use by geological workers to improve work efficiency.展开更多
Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain ...Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain controversial. The present study aimed to study the specific areas involved in naming pictures of these 3 categories using functional magnetic resonance imaging. Methods Functional images were analyzed using statistical parametric mapping and the 3 different contrasts were evaluated using t statistics by comparing the naming tasks to their baselines.The contrast images were entered into a random-effects group level analysis.The results were reported in Montreal Neurological Institute co-ordinates,and anatomical regions were identified using an automated anatomical labeling method with XJview 8.Results Naming famous faces caused more activation in the bilateral head of the hippocampus and amygdala with significant left dominance. Bilateral activation of pars triangularis and pars opercularis in the naming of famous faces was also revealed. Naming animals evoked greater responses in the left supplementary motor area, while naming man-made objects evoked more in the left premotor area,left pars orbitalis and right supplementary motor area. The extent of bilateral fusiform gyri activation by naming man-made objects was much larger than that by naming of famous faces or animals.Even in the overlapping sites of activation,some differences among the categories were found for activation in the fusiform gyri.Conclusion The cortices involved in the naming process vary with the naming of famous faces,animals and man-made objects.This finding suggests that different categories of pictures should be used during intra-operative language mapping to generate a broader map of language function, in order to minimize the incidence of false-negative stimulation and permanent post-operative deficits.展开更多
Kongthong,a remote village hidden in the hills of India's Meghalaya state,has a unique and centuriesold tradition where every inhabitant is given both a regular name and a song at birth,both of which become their ...Kongthong,a remote village hidden in the hills of India's Meghalaya state,has a unique and centuriesold tradition where every inhabitant is given both a regular name and a song at birth,both of which become their identity.Kongthong was nominated(提名)as India's No.1 recommendation for the United Nations World Tourism Organization's Best Tourism Villages contest,both for its natural beauty and hospitable villagers,and its unique naming tradition.展开更多
During its millennium of development,the ancient Shu Road system fostered a unique post station culture.In the Qin and Han dynasties,various postal service facilities,termed you,ting,yi,zhi,and si,emerged along the Ba...During its millennium of development,the ancient Shu Road system fostered a unique post station culture.In the Qin and Han dynasties,various postal service facilities,termed you,ting,yi,zhi,and si,emerged along the Baoxie Road,an important segment of the Shu Road system.In the Tang Dynasty,a dense network of post stations was built along the Shu Road.It was during this period that the post stations gained refined names on a substantial scale.Their names were based on the landscapes,resources,histories,and geographical locations of their sites,all of which displayed the characteristics of local gazetteers.During the Tang and Song dynasties,the post stations became large-scale facilities with well-established functions and beautiful environments.The Shu Road post station culture was carried forward in the Ming and Qing dynasties.However,due to frequent warfare and destruction,the Shu Road post station culture was on a declining trajectory during this period.Additionally,the means of transportation exhibited characteristics that varied with time to adapt to the terrain features of the Shu Road system.During the Qin and Han dynasties,transportation was primarily achieved by courier and horse relays.During the Three Kingdoms period,Zhuge Liang devised wooden oxen and gliding horses to transport military supplies and provisions.In the Tang Dynasty,the administration of horses saw remarkable progress,and horses became the primary means of transportation,supplemented by mail coaches.From the Song to the Ming and Qing dynasties,horses,donkeys,and sedans all served as transportation,reflecting the diversity of transportation means in the later periods of imperial China.展开更多
With the increasing frequency of global medical exchanges,the English translation of clinical medicine disease names has become a key factor affecting medical safety,the quality of academic exchanges,and the level of ...With the increasing frequency of global medical exchanges,the English translation of clinical medicine disease names has become a key factor affecting medical safety,the quality of academic exchanges,and the level of medical education.Meanwhile,there are certain problems in the English translation of clinical medicine disease names in China currently,mainly manifested in the prevalence of multiple translations for one term,prominent issues of literal translation and mistranslation,and inconsistent naming principles.Based on this,this paper focuses on the strategies for standardized English translation of clinical medicine disease names.By explaining the root causes of the problems existing in the current translation field,it further puts forward standardized suggestions from aspects such as establishing authoritative reference standards,following the basic principles of“respecting the originator,following conventions,and adhering to scientific norms”,strengthening medical English education,and using modern tools to assist learning.These efforts aim to gradually unify and standardize disease names,reduce ambiguities and errors,and lay a foundation for international medical exchanges and evidence-based medical research.展开更多
Named entity recognition(NER)in musk deer domain is the extraction of specific types of entities from unstructured texts,constituting a fundamental component of the knowledge graph,Q&A system,and text summarizatio...Named entity recognition(NER)in musk deer domain is the extraction of specific types of entities from unstructured texts,constituting a fundamental component of the knowledge graph,Q&A system,and text summarization system of musk deer domain.Due to limited annotated data,diverse entity types,and the ambiguity of Chinese word boundaries in musk deer domain NER,we present a novel NER model,CAELF-GP,which is based on cross-attention mechanism enhanced lexical features(CAELF).Specifically,we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.In the feature fusion module,instead of indiscriminately merging external dictionary information,we innovatively adopted a feature fusion method based on a cross-attention mechanism,which guides the model to focus on important lexical information by calculating the correlation between each character and its corresponding word sets.This module enhances the model’s semantic representation ability and entity boundary recognition capability.Ultimately,we introduce the decoding module of GlobalPointer(GP)for entity type recognition,capable of identifying both nested and non-nested entities.Since there is currently no publicly available dataset for the musk deer domain,we built a named entity recognition dataset for this domain by collecting relevant literature and working under the guidance of domain experts.The dataset facilitates the training and validation of the model and provides data foundation for subsequent related research.The model undergoes experimentation on two public datasets and the dataset of musk deer domain.The results show that it is superior to the baseline models,offering a promising technical avenue for the intelligent recognition of named entities in the musk deer domain.展开更多
In the research and production of fluorinated materials,large volumes of unstructured textual data are generated,characterized by high heterogeneity and fragmentation.These issues hinder systematic knowledge integrati...In the research and production of fluorinated materials,large volumes of unstructured textual data are generated,characterized by high heterogeneity and fragmentation.These issues hinder systematic knowledge integration and efficient utilization.Constructing a knowledge graph for fluorinated materials processing is essential for enabling structured knowledge management and intelligent applications.Among its core components,Named Entity Recognition(NER)plays an essential role,as its accuracy directly impacts relation extraction and semantic modeling,which ultimately affects the knowledge graph construction for fluorinated materials.However,NER in this domain faces challenges such as fuzzy entity boundaries,inconsistent terminology,and a lack of high-quality annotated corpora.To address these problems,(i)We first construct a domain-specific NER dataset by combining manual annotation with an improved Easy Data Augmentation(EDA)strategy;(ii)Secondly,we propose a novel model,RRC-ADV,which integrates RoBERTa-wwm for dynamic contextual word representation,adversarial training to improve robustness against boundary ambiguity,and a Residual BiLSTM(ResBiLSTM)to enhance sequential feature modeling.Further,a Conditional Random Field(CRF)layer is incorporated for globally optimized label prediction.Experimental results demonstrate that RRC-ADV achieves an average F1 score of 89.23%on the self-constructed dataset,significantly outperforming baseline models.The model exhibits strong robustness and adaptability within the domain of fluorinated materials.Our work enhances the accuracy of NER in the fluorinated materials processing domain and paves the way for downstream tasks such as relation extraction in knowledge graph construction.展开更多
Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to ...Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.展开更多
Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications li...Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.展开更多
The authors regret that the scientific names of some species mentioned in the paper were incorrectly presented.The incorrect sci-entific names,their locations in the paper,correct spellings and refer-ences,are listed ...The authors regret that the scientific names of some species mentioned in the paper were incorrectly presented.The incorrect sci-entific names,their locations in the paper,correct spellings and refer-ences,are listed below.展开更多
Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently r...Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).展开更多
In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to er...In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to error propagation.To overcome the limitations of traditional pipeline models,recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework.To support future research,this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction.The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream joint extraction methods,including joint decoding methods and parameter sharing methods,with joint decoding methods further divided into table filling,tagging,and sequence-to-sequence approaches.In addition,this paper also conducts small-scale replication experiments on models that have performed well in recent years for each method to verify the reproducibility of the code and to compare the performance of different models under uniform conditions.Each method has its own advantages in terms of model design,task handling,and application scenarios,but also faces challenges such as processing complex sentence structures,cross-sentence relation extraction,and adaptability in low-resource environments.Finally,this paper systematically summarizes each method and discusses the future development prospects of joint extraction of relational triples.展开更多
Medical Named Entity Recognition(NER)plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions.Federated Learning(FL)enables collaborati...Medical Named Entity Recognition(NER)plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions.Federated Learning(FL)enables collaborative modeling and training across multiple endpoints without exposing the original data.However,the statistical heterogeneity exhibited by clinical medical text records poses a challenge for FL methods to support the training of NER models in such scenarios.We propose a Federated Contrast Enhancement(FedCE)method for NER to address the challenges faced by non-large-scale pre-trained models in FL for labelheterogeneous.The method leverages a multi-view encoder structure to capture both global and local semantic information,and employs contrastive learning to enhance the interoperability of global knowledge and local context.We evaluate the performance of the FedCE method on three real-world clinical record datasets.We investigate the impact of factors,such as pooling methods,maximum input text length,and training rounds on FedCE.Additionally,we assess how well FedCE adapts to the base NER models and evaluate its generalization performance.The experimental results show that the FedCE method has obvious advantages and can be effectively applied to various basic models,which is of great theoretical and practical significance for advancing FL in healthcare settings.展开更多
文摘In this paper,the geographic name in Southwest China is regarded as a symbolic representation of human beings,and the dynamic social and historical process behind the place names is restored from the perspective of the symbolic anthropology.There are three paths in the construction and evolution of geographic names in Southwest China—Ethnic information,sacred systems,and local representation,which have been rewritten,masked,and reconstructed over the years.As a result,the system of geographical names is gradually formed and integrated into local memory through space building,culture filling,and so on,affecting and influencing local group identity and cognitive concept.
基金Sponsored by the National Grand Fundamental Research 973 Program of China ( Grant No. 2007CB307101-1)the Fundamental Research Funds in Beijing Jiaotong University ( Grant No. W11JB00630)
文摘Research on the next generation network architecture is a hot topic. To meet the requirements of the new Internet environment and eliminate the shortcomings of the existing network, integrated network is presented. In the naming system part, a system based on Chord algorithm was used, and multi-path is introduced to improve the name resolution reliability. In this paper, we mainly pay attention to the reliability model of integrated naming network system which can be attributed as a multi-path transmission issue, and the name resolution paths used in the network path may be cut off by attacks or other events. This paper focuses on parallel multi-path, which is recoverable when failure happens, transmission reliability, and proposes a corresponding reliability model to get the probability of successful transmission in such conditions. Finally, a numerical simulation is devised to demonstrate the multi-path name resolution's high reliability.
基金funded by the National Basic Research Program of China ("973"Program) under Grant No. 2007CB307100
文摘Academia has recently proposed new naming systems based on flat Distributed Hash Table （DHT）. These naming systems are designed to overcome defects--such as lack of support for data migration and replication--in the Domain Name System （DNS）. DHT naming systems have long resolution delay and are not suitable for practical application. This paper introduces two new naming systems that have the advantages of both DNS and DHT systems. The first is a three-layer system based on one-hop DHT and is suitable for small-scale application. The second adopts a hybrid DHT structure, can be implemented in different domains, and can be applied globally. Theoretical analyses demonstrate that these two systems are feasible for practical use.
文摘Traditional taxonomic sorting of samples into recognizable taxonomic units, such as morphospecies or morphotypes, is commonly relied upon in conservation biology and ethnobiological studies. However, understanding the criteria used for traditional nomenclature of fungi, particularly wild edible mushrooms across linguistic groups, remains limited, leading to frequent errors in species recognition. This study seeks to assess how linguistic affiliations influence the local naming of useful wild mushrooms, and is the first of its kind in Benin. In order to understand how local people recognize, classify and name mushrooms that develop in or close to their villages, 2234 respondents from five socio-linguistic groups across three geographical areas were interviewed. Structured and semi-structured interviews were conducted to gather data on the local naming criteria for edible wild mushrooms. Citation scores were recorded for both nomenclature criteria and species, considering variables such as linguistic groups, age, and language. Twenty-two nomenclature criteria were used by local people to name edible wild species. Strong similarity in classification and naming of species was shown in 97% of the languages, while 3% showed differing classification criteria. The Gur, Atlantic, and Mande linguistic groups demonstrated more comprehensive traditional taxonomic and nomenclatural knowledge, sharing six common criteria: texture, taste, size, kingdom (Fungi), form, and substrate. Overall, local populations possess extensive knowledge regarding the diversity of wild edible mushrooms in their environment.
基金Supported by the Qinghai Province Geological Exploration Fund Project(2023085029ky004)Natural Science Foundation of Jilin Provinc(20220101161JC)Open Project Plan of Shandong Province Deep Gold Exploration in Big Data Application and Development Engineering Laboratory(SDK202203).
文摘The naming of rock hand specimens is usually conducted by geological workers based on observed mineral composition,texture characteristics,etc.,combined with their own knowledge reserves.The accuracy of identification results is limited by the experience,research interests,and identification level of the identifier,as well as the complexity of the rock composition.To improve the efficiency of rock hand specimen identification,this paper proposes a method for rock image recognition and classification based on deep learning and the Inception-v3 model.It encompasses the preprocessing of collected photographs of typical intrusive rock hand specimens,along with augmenting the sample size through data augmentation methods,culminating in a comprehensive dataset comprising 12501 samples.Experimental results show that the model has good learning ability when there is sufficient data.Through iterative training of the Inception-v3 model on the rock dataset,the accuracy of rock image recognition reaches 92.83%,with a loss of only 0.2156.Currently,several common types of intrusive rocks can be identified:gabbro,granite,diorite,peridotite,granodiorite,diabase,and granite porphyry.Software is developed for open use by geological workers to improve work efficiency.
基金supported bythe Foundation of Science and Technology Program of Guangdong Province,China(No.2008A030201021)the Natural Science Foundation of Guangdong Province,China(No.10151001002000010)
文摘Objective Category-specific recognition and naming deficits have been observed in a variety of patient populations. However, the category-specific cortices for naming famous faces, animals and man-made objects remain controversial. The present study aimed to study the specific areas involved in naming pictures of these 3 categories using functional magnetic resonance imaging. Methods Functional images were analyzed using statistical parametric mapping and the 3 different contrasts were evaluated using t statistics by comparing the naming tasks to their baselines.The contrast images were entered into a random-effects group level analysis.The results were reported in Montreal Neurological Institute co-ordinates,and anatomical regions were identified using an automated anatomical labeling method with XJview 8.Results Naming famous faces caused more activation in the bilateral head of the hippocampus and amygdala with significant left dominance. Bilateral activation of pars triangularis and pars opercularis in the naming of famous faces was also revealed. Naming animals evoked greater responses in the left supplementary motor area, while naming man-made objects evoked more in the left premotor area,left pars orbitalis and right supplementary motor area. The extent of bilateral fusiform gyri activation by naming man-made objects was much larger than that by naming of famous faces or animals.Even in the overlapping sites of activation,some differences among the categories were found for activation in the fusiform gyri.Conclusion The cortices involved in the naming process vary with the naming of famous faces,animals and man-made objects.This finding suggests that different categories of pictures should be used during intra-operative language mapping to generate a broader map of language function, in order to minimize the incidence of false-negative stimulation and permanent post-operative deficits.
文摘Kongthong,a remote village hidden in the hills of India's Meghalaya state,has a unique and centuriesold tradition where every inhabitant is given both a regular name and a song at birth,both of which become their identity.Kongthong was nominated(提名)as India's No.1 recommendation for the United Nations World Tourism Organization's Best Tourism Villages contest,both for its natural beauty and hospitable villagers,and its unique naming tradition.
基金This paper is a phased achievement of the project“Organization and Research of the Shu Road Literature”(Project No.17ZDA190),a major project of the National Social Science Fund of China.
文摘During its millennium of development,the ancient Shu Road system fostered a unique post station culture.In the Qin and Han dynasties,various postal service facilities,termed you,ting,yi,zhi,and si,emerged along the Baoxie Road,an important segment of the Shu Road system.In the Tang Dynasty,a dense network of post stations was built along the Shu Road.It was during this period that the post stations gained refined names on a substantial scale.Their names were based on the landscapes,resources,histories,and geographical locations of their sites,all of which displayed the characteristics of local gazetteers.During the Tang and Song dynasties,the post stations became large-scale facilities with well-established functions and beautiful environments.The Shu Road post station culture was carried forward in the Ming and Qing dynasties.However,due to frequent warfare and destruction,the Shu Road post station culture was on a declining trajectory during this period.Additionally,the means of transportation exhibited characteristics that varied with time to adapt to the terrain features of the Shu Road system.During the Qin and Han dynasties,transportation was primarily achieved by courier and horse relays.During the Three Kingdoms period,Zhuge Liang devised wooden oxen and gliding horses to transport military supplies and provisions.In the Tang Dynasty,the administration of horses saw remarkable progress,and horses became the primary means of transportation,supplemented by mail coaches.From the Song to the Ming and Qing dynasties,horses,donkeys,and sedans all served as transportation,reflecting the diversity of transportation means in the later periods of imperial China.
文摘With the increasing frequency of global medical exchanges,the English translation of clinical medicine disease names has become a key factor affecting medical safety,the quality of academic exchanges,and the level of medical education.Meanwhile,there are certain problems in the English translation of clinical medicine disease names in China currently,mainly manifested in the prevalence of multiple translations for one term,prominent issues of literal translation and mistranslation,and inconsistent naming principles.Based on this,this paper focuses on the strategies for standardized English translation of clinical medicine disease names.By explaining the root causes of the problems existing in the current translation field,it further puts forward standardized suggestions from aspects such as establishing authoritative reference standards,following the basic principles of“respecting the originator,following conventions,and adhering to scientific norms”,strengthening medical English education,and using modern tools to assist learning.These efforts aim to gradually unify and standardize disease names,reduce ambiguities and errors,and lay a foundation for international medical exchanges and evidence-based medical research.
基金funded by 5·5 Engineering Research&Innovation Team Project of Beijing Forestry University(No.BLRC2023C02).
文摘Named entity recognition(NER)in musk deer domain is the extraction of specific types of entities from unstructured texts,constituting a fundamental component of the knowledge graph,Q&A system,and text summarization system of musk deer domain.Due to limited annotated data,diverse entity types,and the ambiguity of Chinese word boundaries in musk deer domain NER,we present a novel NER model,CAELF-GP,which is based on cross-attention mechanism enhanced lexical features(CAELF).Specifically,we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.In the feature fusion module,instead of indiscriminately merging external dictionary information,we innovatively adopted a feature fusion method based on a cross-attention mechanism,which guides the model to focus on important lexical information by calculating the correlation between each character and its corresponding word sets.This module enhances the model’s semantic representation ability and entity boundary recognition capability.Ultimately,we introduce the decoding module of GlobalPointer(GP)for entity type recognition,capable of identifying both nested and non-nested entities.Since there is currently no publicly available dataset for the musk deer domain,we built a named entity recognition dataset for this domain by collecting relevant literature and working under the guidance of domain experts.The dataset facilitates the training and validation of the model and provides data foundation for subsequent related research.The model undergoes experimentation on two public datasets and the dataset of musk deer domain.The results show that it is superior to the baseline models,offering a promising technical avenue for the intelligent recognition of named entities in the musk deer domain.
基金funded by the Opening Fund of Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things(No.2023WYJ06)the Yadong Wu Talent Program(No.H31225001)+1 种基金supported in part by the Defense Industrial Technology Development Program(No.JCKY2022404C001)by the Sichuan Provincial Key Lab of Process Equipment and Control’s Project(No.GK201509)。
文摘In the research and production of fluorinated materials,large volumes of unstructured textual data are generated,characterized by high heterogeneity and fragmentation.These issues hinder systematic knowledge integration and efficient utilization.Constructing a knowledge graph for fluorinated materials processing is essential for enabling structured knowledge management and intelligent applications.Among its core components,Named Entity Recognition(NER)plays an essential role,as its accuracy directly impacts relation extraction and semantic modeling,which ultimately affects the knowledge graph construction for fluorinated materials.However,NER in this domain faces challenges such as fuzzy entity boundaries,inconsistent terminology,and a lack of high-quality annotated corpora.To address these problems,(i)We first construct a domain-specific NER dataset by combining manual annotation with an improved Easy Data Augmentation(EDA)strategy;(ii)Secondly,we propose a novel model,RRC-ADV,which integrates RoBERTa-wwm for dynamic contextual word representation,adversarial training to improve robustness against boundary ambiguity,and a Residual BiLSTM(ResBiLSTM)to enhance sequential feature modeling.Further,a Conditional Random Field(CRF)layer is incorporated for globally optimized label prediction.Experimental results demonstrate that RRC-ADV achieves an average F1 score of 89.23%on the self-constructed dataset,significantly outperforming baseline models.The model exhibits strong robustness and adaptability within the domain of fluorinated materials.Our work enhances the accuracy of NER in the fluorinated materials processing domain and paves the way for downstream tasks such as relation extraction in knowledge graph construction.
基金supported in part by the National Science and Technology Major Project under(Grant 2022ZD0116100)in part by the National Natural Science Foundation Key Project under(Grant 62436006)+4 种基金in part by the National Natural Science Foundation Youth Fund under(Grant 62406257)in part by the Xizang Autonomous Region Natural Science Foundation General Project under(Grant XZ202401ZR0031)in part by the National Natural Science Foundation of China under(Grant 62276055)in part by the Sichuan Science and Technology Program under(Grant 23ZDYF0755)in part by the Xizang University‘High-Level Talent Training Program’Project under(Grant 2022-GSP-S098).
文摘Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.
基金funded by Advanced Research Project(30209040702).
文摘Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.
文摘The authors regret that the scientific names of some species mentioned in the paper were incorrectly presented.The incorrect sci-entific names,their locations in the paper,correct spellings and refer-ences,are listed below.
基金supported in part by the National Natural Science Foundation of China under Grant 61972424 and 62372479in part by the High Value Intellectual Property Cultivation Project of Hubei Province,China,under grant D2021002094+1 种基金in part by JSPS KAKENHI under Grants JP16K00117 and JP19K20250in part by the Leading Initiative for Excellent Young Researchers(LEADER),MEXT,Japan,and KDDI Foundation.
文摘Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).
基金funding from Key Areas Science and Technology Research Plan of Xinjiang Production And Construction Corps Financial Science and Technology Plan Project under Grant Agreement No.2023AB048 for the project:Research and Application Demonstration of Data-driven Elderly Care System.
文摘In recent years,with the rapid development of deep learning technology,relational triplet extraction techniques have also achieved groundbreaking progress.Traditional pipeline models have certain limitations due to error propagation.To overcome the limitations of traditional pipeline models,recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework.To support future research,this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction.The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream joint extraction methods,including joint decoding methods and parameter sharing methods,with joint decoding methods further divided into table filling,tagging,and sequence-to-sequence approaches.In addition,this paper also conducts small-scale replication experiments on models that have performed well in recent years for each method to verify the reproducibility of the code and to compare the performance of different models under uniform conditions.Each method has its own advantages in terms of model design,task handling,and application scenarios,but also faces challenges such as processing complex sentence structures,cross-sentence relation extraction,and adaptability in low-resource environments.Finally,this paper systematically summarizes each method and discusses the future development prospects of joint extraction of relational triples.
基金supported by the National Key Research and Development Program of China(Nos.2023YFC3502604,2022YFC2403902,2020YFC0841600,and 2020YFC0845000-4)the National Natural Science Foundation of China(Nos.82374302,82174533,82204941,and U23B2062)+3 种基金the Natural Science Foundation of Beijing(No.L232033)the Key R&D project of Ningxia Autonomous Region(No.2022BEG02036)the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0505700)the Fundamental Research Funds for the Central Universities(No.2024JBMC007).
文摘Medical Named Entity Recognition(NER)plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions.Federated Learning(FL)enables collaborative modeling and training across multiple endpoints without exposing the original data.However,the statistical heterogeneity exhibited by clinical medical text records poses a challenge for FL methods to support the training of NER models in such scenarios.We propose a Federated Contrast Enhancement(FedCE)method for NER to address the challenges faced by non-large-scale pre-trained models in FL for labelheterogeneous.The method leverages a multi-view encoder structure to capture both global and local semantic information,and employs contrastive learning to enhance the interoperability of global knowledge and local context.We evaluate the performance of the FedCE method on three real-world clinical record datasets.We investigate the impact of factors,such as pooling methods,maximum input text length,and training rounds on FedCE.Additionally,we assess how well FedCE adapts to the base NER models and evaluate its generalization performance.The experimental results show that the FedCE method has obvious advantages and can be effectively applied to various basic models,which is of great theoretical and practical significance for advancing FL in healthcare settings.
