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
The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise t...The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.展开更多
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
Dear Jack,I'm very glad to know that you'll come to China to learn Chinese.And you want to know about Chinese names.Now,I'd like to tell you something about them.Chinese names are different from English na...Dear Jack,I'm very glad to know that you'll come to China to learn Chinese.And you want to know about Chinese names.Now,I'd like to tell you something about them.Chinese names are different from English names.In Chinese,family names always come first and given names come last Given names usually have some special meanings.We also had informal names when we were little kids,such as Congcong,Nana and so on.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
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
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
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
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
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
Karst phenomena occurring on land surface create sinkholes,ground fissures and other hazardous events.Dissolution of gypsum of Upper Devonian formations in North Lithuania,that occur under thin Quaternary cover result...Karst phenomena occurring on land surface create sinkholes,ground fissures and other hazardous events.Dissolution of gypsum of Upper Devonian formations in North Lithuania,that occur under thin Quaternary cover results with rapid occurrence of hazardous sinkholes.Monitoring of karst phenomena in Lithuania includes measurements of volumes of karst sinkholes(cubic meters)and amount of dissolved underground gypsum–named chemical gypsum denudation measured by amount cubic meters of gypsum dissolved from 1 square kilometer of karst terrain during one year.展开更多
Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or ...Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches.In this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge base.It also extracts vision-based auxiliary knowledge from the image formore accurate and effective recognition.Specifically,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs.Furthermore,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased encoder.Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNER datasets,even when the largemodels employed have significantly fewer parameters than other baselines.展开更多
The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system.Aimed at the characteristics of Chinese poetry ...The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system.Aimed at the characteristics of Chinese poetry and wine culture entities with different lengths and high training cost of named entity recognition models at the present stage,this study proposes a lite BERT+bi-directional long short-term memory+attentional mechanisms+conditional random field(ALBERT+BILSTM+Att+CRF).The method first obtains the characterlevel semantic information by ALBERT module,then extracts its high-dimensional features by BILSTM module,weights the original word vector and the learned text vector by attention layer,and finally predicts the true label in CRF module(including five types:poem title,author,time,genre,and category).Through experiments on data sets related to Chinese poetry and wine culture,the results show that the method is more effective than existing mainstream models and can efficiently extract important entity information in Chinese poetry and wine culture,which is an effective method for the identification of named entities of varying lengths of poetry.展开更多
As a Chinese proverb declares,"The beginningof wisdom is to call things by their right names."For an entrepreneur,the beginning of his success isto call his brand by a right name.A brand needs agood name as ...As a Chinese proverb declares,"The beginningof wisdom is to call things by their right names."For an entrepreneur,the beginning of his success isto call his brand by a right name.A brand needs agood name as much as the mankind does.A展开更多
This paper presents the different categories of communicative names of cities and streets with some examples all over the world. The commemorative role is playing also by the names of some continents, like America, fr...This paper presents the different categories of communicative names of cities and streets with some examples all over the world. The commemorative role is playing also by the names of some continents, like America, from the Amerigo Vespucci＇s name. But many countries and states particularly in the Americas remember the Columbus＇ name. Many city names remember political leaders of great significance, like Washington after Georg Washington, Monrovia after James Monroe, Leningrad （today Saint Petersburg, which remembered the tsar Peter I）. Two soviet leaders, Lenin and Stalin were remembered by two Russian cities, Leningrad and Stalingrad, nowadays Saint Petersburg and Volgograd. The name Ho Chi Minh, after the North Vietnamese leader. The names of other regions and cities have been considered, like many cities and islands named Georgia after some kings. Also Louisiana, The Carolinas, Lfopoldville, Elisabethville were named after other kings and queens. Some cities and islands＇ names remember explorers, like Cook, Stanley, Louren^o Marques （today Maputo, after the name of a fiver）, Brazzaville after, Pietro Paolo Savorgnan di Bra77h etc. Rio de Janeiro is named after the date of its discovery. Many place names were named after Alexander the Great, outer than the Italian Alesssandria, named after the Pope Alexander III. Many place names remember Saints, particularly in Latin America, but also in North America, like St. Francisco. The streets＇ names are many all over the world. In Rome we have streets names to politicians, like Camill Benso di Cavour, Giuseppe Mazzini and Palmiro Togliatti; writers like Alessandro Manzoni and Torquato Tasso. A name like Giuseppe Garibaldi is extremely diffused in all Italy and also in other countries.展开更多
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MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).
文摘The context of recognizing handwritten city names,this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script.In today’s technology-driven era,where precise tools for reading handwritten text are essential,this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting.The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems,particularly in critical areas such as postal automation and document processing.Notably,no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition.To bridge this gap,the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition.The emphasis on practical data for system training enhances accuracy.The research further conducts a comparative analysis,pitting state-of-the-art(SOTA)deep learning models,including EfficientNetB0,VGG16,ResNet50,DenseNet201,InceptionV3,and Xception,against a custom Convolutional Neural Networks(CNN)model named“Our CNN.”The results showcase the superior performance of“Our CNN,”with a test accuracy of 99.97% and an outstanding F1 score of 99.95%.These metrics underscore its potential for automating city name recognition,particularly in postal services.The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures.It encourages future research avenues,including dataset expansion,algorithm refinement,exploration of recurrent neural networks and attention mechanisms,real-world deployment of models,and extension to other regional languages and scripts.These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.
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Dear Jack,I'm very glad to know that you'll come to China to learn Chinese.And you want to know about Chinese names.Now,I'd like to tell you something about them.Chinese names are different from English names.In Chinese,family names always come first and given names come last Given names usually have some special meanings.We also had informal names when we were little kids,such as Congcong,Nana and so on.
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.
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.
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.
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.
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.
基金supported by the Ministry of Education,Science and Sport of Republic of Lithuania under the program“An influence of the climate of the climatic and anthropogenic driven factors on the ecosystems and their behaviors,services provided and sustainability of the resources”(20220419/V-585)The monitoring of karst denudation and mapping of sinkholes was funded by Lithuanian Geological Survey under Ministry of Environment,Republic of Lithuania.
文摘Karst phenomena occurring on land surface create sinkholes,ground fissures and other hazardous events.Dissolution of gypsum of Upper Devonian formations in North Lithuania,that occur under thin Quaternary cover results with rapid occurrence of hazardous sinkholes.Monitoring of karst phenomena in Lithuania includes measurements of volumes of karst sinkholes(cubic meters)and amount of dissolved underground gypsum–named chemical gypsum denudation measured by amount cubic meters of gypsum dissolved from 1 square kilometer of karst terrain during one year.
基金funded by Research Project,grant number BHQ090003000X03.
文摘Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches.In this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge base.It also extracts vision-based auxiliary knowledge from the image formore accurate and effective recognition.Specifically,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs.Furthermore,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased encoder.Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNER datasets,even when the largemodels employed have significantly fewer parameters than other baselines.
基金the Sichuan Science and Technology Program of China(No.2021YFG0055)the Zigong Science and Technology Program of China(No.2019YYJC15)+1 种基金the Nature Science Foundation of Sichuan University of Science&Engineering(No.2020RC32)the 2022 Graduate Innovation Fund Project of Sichuan University of Science&Engineering(No.Y2022168)。
文摘The task of identifying Chinese named entities of Chinese poetry and wine culture is a key step in the construction of a knowledge graph and a question and answer system.Aimed at the characteristics of Chinese poetry and wine culture entities with different lengths and high training cost of named entity recognition models at the present stage,this study proposes a lite BERT+bi-directional long short-term memory+attentional mechanisms+conditional random field(ALBERT+BILSTM+Att+CRF).The method first obtains the characterlevel semantic information by ALBERT module,then extracts its high-dimensional features by BILSTM module,weights the original word vector and the learned text vector by attention layer,and finally predicts the true label in CRF module(including five types:poem title,author,time,genre,and category).Through experiments on data sets related to Chinese poetry and wine culture,the results show that the method is more effective than existing mainstream models and can efficiently extract important entity information in Chinese poetry and wine culture,which is an effective method for the identification of named entities of varying lengths of poetry.
文摘As a Chinese proverb declares,"The beginningof wisdom is to call things by their right names."For an entrepreneur,the beginning of his success isto call his brand by a right name.A brand needs agood name as much as the mankind does.A
文摘This paper presents the different categories of communicative names of cities and streets with some examples all over the world. The commemorative role is playing also by the names of some continents, like America, from the Amerigo Vespucci＇s name. But many countries and states particularly in the Americas remember the Columbus＇ name. Many city names remember political leaders of great significance, like Washington after Georg Washington, Monrovia after James Monroe, Leningrad （today Saint Petersburg, which remembered the tsar Peter I）. Two soviet leaders, Lenin and Stalin were remembered by two Russian cities, Leningrad and Stalingrad, nowadays Saint Petersburg and Volgograd. The name Ho Chi Minh, after the North Vietnamese leader. The names of other regions and cities have been considered, like many cities and islands named Georgia after some kings. Also Louisiana, The Carolinas, Lfopoldville, Elisabethville were named after other kings and queens. Some cities and islands＇ names remember explorers, like Cook, Stanley, Louren^o Marques （today Maputo, after the name of a fiver）, Brazzaville after, Pietro Paolo Savorgnan di Bra77h etc. Rio de Janeiro is named after the date of its discovery. Many place names were named after Alexander the Great, outer than the Italian Alesssandria, named after the Pope Alexander III. Many place names remember Saints, particularly in Latin America, but also in North America, like St. Francisco. The streets＇ names are many all over the world. In Rome we have streets names to politicians, like Camill Benso di Cavour, Giuseppe Mazzini and Palmiro Togliatti; writers like Alessandro Manzoni and Torquato Tasso. A name like Giuseppe Garibaldi is extremely diffused in all Italy and also in other countries.
