Should the Writing of Chinese Names in Pinyin Be Standardized?

With international exchanges and gatherings becoming more common, Chinese in pinyin, mainly the names of people and places has become more and more common. Pinyin is a system for transliterating Chinese characters into the Roman alphabet and was officially adopted by the People＇s Republic of China in 1979.

Contrastive Analysis of Chinese and British Names from the Perspective of Culture

A name can reflect a nation's history and culture. Studying the differences and similarities of names between two countries can help us have a better understanding of the two cultures and thus promote further communications. A contrastive analysis of Chinese and British names are discussed mainly from the forms, origins and the factors influencing naming. It is founded that the differences of names are closely related with history, religion, social system and values while the similarities are focus on the common things. In general, there are more differences than similarities. The naming system has a long history and as a traditional culture it truly deserves our attention.

An Experimental Pragmatic Study of Readers’Preferences for Methods Used in Translating Chinese Brand Names

This survey investigated different methods for translating Chinese brand names into English.Results of Pre-investigation show that five methods are most frequently used in translating Chinese Brand Names into English:English,Pinyin,Coinage,Acronym,and English+Pinyin.Two further experiments were conducted.The results of Experiment 1 indicated that participants evaluated translations produced using English as their most favored and English as the most appropriate method to translate Chinese brand names,showing low interest in translations by Pinyin and Coinage.The results of Experiment 2 further supported the English method as the most favored one in comparison to the Acronym and English+Pinyin,methods,and likewise in the methods used to translate different categories of brands.A“Mother-Tongue”Effect is observed in translation.This study concludes that English is the most effective method for translating Chinese brand names.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

Similarities and Dissimilarities between Chinese and British Names

The name is the unique cultural phenomenon in human society.One’sOneooOoOOOOOOOO name not only a sign used to dis⁃tinguish him/her from others,but also a mark accompanying his/her whole life.It includes much information which implies the nation’s language,history,geography,religion,and cultural tradition.Research into the names of different nationalities will help us deepen the understanding of cultures.The commonness of Chinese and British cultures determines the similarities of Chinese and Britain names,while the different historical cultures,value ideas,thinking patterns,religion,and psychology determine the dissimilarities.This paper will discuss the similarities and dissimilarities between Chinese and British names from several aspects and analyze the cultural differences reflected by Chinese and British names.

Chinese named entity recognition with multi-network fusion of multi-scale lexical information

Named entity recognition(NER)is an important part in knowledge extraction and one of the main tasks in constructing knowledge graphs.In today’s Chinese named entity recognition(CNER)task,the BERT-BiLSTM-CRF model is widely used and often yields notable results.However,recognizing each entity with high accuracy remains challenging.Many entities do not appear as single words but as part of complex phrases,making it difficult to achieve accurate recognition using word embedding information alone because the intricate lexical structure often impacts the performance.To address this issue,we propose an improved Bidirectional Encoder Representations from Transformers(BERT)character word conditional random field(CRF)(BCWC)model.It incorporates a pre-trained word embedding model using the skip-gram with negative sampling(SGNS)method,alongside traditional BERT embeddings.By comparing datasets with different word segmentation tools,we obtain enhanced word embedding features for segmented data.These features are then processed using the multi-scale convolution and iterated dilated convolutional neural networks(IDCNNs)with varying expansion rates to capture features at multiple scales and extract diverse contextual information.Additionally,a multi-attention mechanism is employed to fuse word and character embeddings.Finally,CRFs are applied to learn sequence constraints and optimize entity label annotations.A series of experiments are conducted on three public datasets,demonstrating that the proposed method outperforms the recent advanced baselines.BCWC is capable to address the challenge of recognizing complex entities by combining character-level and word-level embedding information,thereby improving the accuracy of CNER.Such a model is potential to the applications of more precise knowledge extraction such as knowledge graph construction and information retrieval,particularly in domain-specific natural language processing tasks that require high entity recognition precision.

Translation of Chinese Periodical Names

Anhui Shifan Daxue xuebao: Journal of AnhiNormal University (humanities and socialscience edition)

Translation of Chinese Periodical Names

Bainian chao: Hundred Year Tide Beijing Daxue xuebao: Journal of Beijing University (humanities and social science edition)

Translation of Chinese Periodical Names

Bainian chao: Hundred Year Tide Beijing Daxue xuebao: Journal of Beijing University (humanities and social science edition)

Translation of Chinese Periodical Names

Anhui Shifan Daxue xuebao: Journal of AnhuiNormal University (humanities and socialscience edition) Beijing Daxue xuebao: Journal of BeijingUniversity (humanities and social sciencesedition) Beijing shehui kexue: Social Sciences ofBeijing Beijing Shifan Daxue xuebao: Journal ofBeijing Normal University (humanities andsocial sciences edition)

Translation of Chinese Periodical Names

Anhui Shifan Daxue xuebao: Journal of AnhuiNormal University (humanities and socialscience edition) Beijing Daxue xuebao: Journal of BeijingUniversity (humanities and social sciencesedition) Beijing shehui kexue: Social Sciences ofBeijing Beijing Shifan Daxue xuebao: Journal ofBeijing Normal University (humanities andsocial sciences edition)

Translation of Chinese Periodical Names

Anhui Shifan Daxue xuebao: Journal of AnhuiNormal University (humanities and socialscience edition)Beijing Daxue xuebao: Journal of BeijingUniversity (humanities and social sciencesedition)Beijing shehui kexue:Social Sciences ofBeijingBeijing Shifan Daxue xuebao: Journal ofBeijing Normal University (humanities andsocial sciences edition)Caijing luncong: Financial and

Translation of Chinese Periodical Names

Anhui Shifan Daxue xuebao: Journal of AnhuiNormal University (humanities and socialscience edition) Beijing Daxue xuebao: Journal of BeijingUniversity (humanities and social sciencesedition) Beijing shehui kexue: Social Sciences ofBeijing Beijing Shifan Daxue xuebao: Journal ofBeijing Normal University (humanities

Transiation of Chinese Periodical Names

Anhui Shifan Daxuexuebao:Journal ofAnhui Normal University(humanities and social science edition)