A Cognitive Study of the Naming Elements of Chinese Dish Names Based on the Prominence Principle

The assortedness of Chinese food,together with the complexity of their naming elements,has ignited numerous scholars' interests in this field and prompted them to make abundant analyses of Chinese dish names.Most of them,however,were done in studies of traditional linguistics,rhetoric,translatology and cross-cultural communication.And studies,based on corpus,on the naming elements of Chinese dishes under cognitive linguistic theories almost remain a blank.This paper aims to conduct a quantitative analysis of 4,000 Chinese dish names(500 ones selected freely from each of the eight cuisines),based on the Prominence Principle,in order to identify the specific naming elements of Chinese dishes and forward related statistics and ratios.

Geographical Names as Cultural Symbols:The Law of Naming System’s Evolution in Southwest China

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.

Naming Conventions and the Perception of Selfhood:A Cross-cultural Reflection on Women’s Surnames in the Anglosphere vs.the Hispanic Model

The naming convention in English-speaking countries(e.g.,USA and UK),and several others in the Western culture,where women traditionally have adopted their husbands’surnames,is compared with the naming convention in Spain and Latin America,where women do not relinquish their maiden surnames.From a cross-cultural perspective spanning over three centuries,from Madame de Staël and Virginia Woolf to Hillary Clinton,this essay renders instances of women who took on the surname of their spouse upon marriage.It appears that even nowadays many women,including feminists,choose to comply with this patriarchal habit.Entanglements arising upon divorce or remarriage,such as traceability and perception of selfhood,especially for women with academic and professional profiles,are discussed here.Samples collected from life and literature across a fairly representative cultural range and diverse moments in history help to reach conclusions and come up with a consistent argument.Winds of change seem to be blowing with Vice President Kamala Harris,whose case is mentioned at the end of this essay.To circumvent the confusion for individuals and families(especially“blended”ones)that could result in the discrimination between males and females,on the one hand,and on the other hand,between married and unmarried women,the Spanish naming convention is proposed as a perfect compromise.This consists in every person bearing two surnames from birth and for good:one of each parent.Thus,women would keep their name(s),and along with them their perception of their self and their social and professional identity.

SciCN:A Scientific Dataset for Chinese Named Entity Recognition

Named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent years.The abundant annotated English NER datasets have significantly promoted the NER research in the English field.By contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER datasets.To alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific papers.We manually annotate a total of 62,059 entities,and these entities are classified into six types.Compared to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research fields.To investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate SciCN.Experimental results show that SciCN is more challenging than other Chinese NER datasets.In addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER models.Motivated by this fact,we provide a scientific domain-specific lexicon.Validation results demonstrate that our lexicon delivers better performance gains than lexicons of other domains.We hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future research.The dataset and lexicon are available at:https://github.com/yangjingla/SciCN.git.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

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.

Development of a Lightweight Model for Handwritten Dataset Recognition: Bangladeshi City Names in Bangla Script

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.

GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

Mathematical Named Entity Recognition Based on Adversarial Training and Self-Attention

Mathematical named entity recognition(MNER)is one of the fundamental tasks in the analysis of mathematical texts.To solve the existing problems of the current neural network that has local instability,fuzzy entity boundary,and long-distance dependence between entities in Chinese mathematical entity recognition task,we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long shortterm memory-Selfattention Conditional random field(AT-BSAC)model.In our model,the mathematical text was vectorized by the word embedding technique,and small perturbations were added to the word vector to generate adversarial samples,while local features were extracted by Bi-directional Long Short-Term Memory(BiLSTM).The self-attentive mechanism was incorporated to extract more dependent features between entities.The experimental results demonstrated that the AT-BSAC model achieved a precision(P)of 93.88%,a recall(R)of 93.84%,and an F1-score of 93.74%,respectively,which is 8.73%higher than the F1-score of the previous Bi-directional Long Short-Term Memory Conditional Random Field(BiLSTM-CRF)model.The effectiveness of the proposed model in mathematical named entity recognition.

Something About Chinese Names

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.

A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models

Named Entity Recognition(NER)is crucial for extracting structured information from text.While traditional methods rely on rules,Conditional Random Fields(CRFs),or deep learning,the advent of large-scale Pre-trained Language Models(PLMs)offers new possibilities.PLMs excel at contextual learning,potentially simplifying many natural language processing tasks.However,their application to NER remains underexplored.This paper investigates leveraging the GPT-3 PLM for NER without fine-tuning.We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity.Our experimental results demonstrate the feasibility of this approach,suggesting a promising direction for harnessing PLMs in NER.

移动互联网Naming网络技术的发展

现有互联网架构缺乏统一的主机标识描述处理机制,难以满足应用业务和网络的多样性需求。本文从现有业务需求出发,研究了网络的中期发展趋势,提出了网络演进过程中衍生的技术问题以及移动互联网Naming网络技术发展的特点、适用场景。

Naming Game模型的研究进展及应用

Naming Game模型揭示了智能主体如何通过自组织达成共识,进而促成语言体系的形成。介绍该模型的基本情况和发展脉络,并指出其在语言学、社会学和经济管理领域的应用前景。

黄连碱对L-NAME诱导高血压大鼠胸主动脉功能的影响

目的探讨黄连碱对L-NAME致高血压大鼠胸主动脉舒缩功能的影响。方法采用NO合酶抑制剂L-NAME制备大鼠高血压模型,然后每天灌胃给予黄连碱低(10 mg.kg-1)、中(30 mg.kg-1)、高(100 mg.kg-1)3个剂量;给药干预6周后采用体外血管环活性评价方法,评价各组大鼠胸主动脉的收缩和舒张功能。结果 L-NAME诱导大鼠高血压模型,其胸主动脉的收缩和舒张功能均受到影响。给予黄连碱6周,黄连碱可明显增强L-NAME诱导高血压大鼠血管平滑肌对细胞内钙释放引起的收缩反应,并降低外钙内流引起的血管收缩能力,但对大鼠血压无影响,对血管的收缩功能和内皮依赖的舒张功能影响不大。结论 L-NAME制备大鼠高血压模型主要与NO的减少有关,而黄连碱可能影响IP3-Ca2+通路,对L-NAME诱导大鼠高血压没有降压作用。

一氧化氮合酶抑制剂L-NAME对大鼠脑缺血耐受诱导的影响

采用大鼠四血管闭塞全脑缺血耐受模型和脑组织切片形态学方法 ,观察应用一氧化氮合酶 (NOS)抑制剂L NAME对大鼠海马CA1区脑缺血耐受 (BIT)诱导的影响 ,在整体水平探讨一氧化氮 (NO)在BIT诱导中的作用。 5 4只Wistar大鼠凝闭双侧椎动脉后分为 6组 :( 1)假手术组 (n =6) :分离双侧颈总动脉 ,但不阻断脑血流 ;( 2 )损伤性缺血组 (n =6) :全脑缺血 10min ;( 3 )预缺血 +损伤性缺血组 (n =6) :脑缺血预处理 (CIP) 3min ,再灌注 72h后行全脑缺血 10min ;( 4)L NAME组 :分别于CIP前 1h和后 1、12及 3 6h腹腔注射L NAME ( 5mg/kg) ,每个时间点 6只动物 ,其余步骤同预缺血 +损伤性缺血组 ;( 5 )L NAME +L 精氨酸组 (n =6) :于CIP前 1h腹腔注射L NAME ( 5mg/kg)和L 精氨酸 ( 3 0 0mg/kg) ,其它步骤同L NAME组 ;( 6)L NAME +损伤性缺血组 (n =6) :于腹腔注射L NAME ( 5mg/kg) 72h后行全脑缺血 10min。实验结果表明 ,( 1)单纯 10min全脑缺血可使海马CA1区组织学分级增加 (表明损伤加重 ) ,神经元密度降低 (P <0 0 1) ;( 2 )预缺血 +损伤性缺血组的海马CA1区组织学分级、神经元密度与假手术组相比 ,无显著性差别 (P >0 0 5 ) ;( 3 )L NAME组中 ,应用L NAME后海马CA1区组织学分级增加 ,神经元密度降低 ,与预缺血

L-NAME对沙土鼠海马区cGMP作用的研究

目的：探讨ＮＯＳ抑制剂ＬＮＡＭＥ（左旋硝基精氨酸甲酯）对沙土鼠海马区环磷酸鸟苷（ｃＧＭＰ）的影响。方法：钳夹沙土鼠的双侧颈总动脉制造脑缺血模型，应用免疫荧光法染色，以便观察ｃＧＭＰ浓度的变化及分布。结果：ＬＮＡＭＥ抑制ｃＧＭＰ的合成。结论：ＬＮＡＭＥ是一氧化氮合酶（ＮＯＳ）强有力的抑制剂。

日粮中添加L-NAME对肉鸡腹水综合征发生的影响及其机理

应用低温环境和日粮中添加三碘甲腺原氨酸 (3,3,5 - triiodothyronine,T3 )诱发肉鸡腹水综合征 ,同时通过日粮添加一氧化氮合酶抑制剂 L- NAME,研究了抑制 NO的合成对肉鸡肺动脉压及肉鸡腹水综合征发生的影响。 180羽AA肉鸡随机等分为对照组 (C)、试验组 (A、B) ,参试鸡 14日龄前按常规条件饲养。14日龄后 C组鸡按常规条件饲养 ,A、B组鸡舍温按每日 1～ 2℃逐步降至 12℃ ,同时日粮中添加 T3 1.5 m g/ kg以诱发腹水综合征。另外 ,B组肉鸡从 14日龄起在日粮中添加 10 0 mg/ kg L- NAME至试验结束。分别于 3、4、5、6周龄测定各组肉鸡肺动脉平均压 (mean pul-monary arterial pressure,m PAP)、红细胞压积、右心全心比、血浆一氧化氮 (NO)和内皮素 (endothelin- 1,ET- 1)水平并记录腹水综合征发病率。结果显示 :低温添加 T3 处理后 ,A、B组肉鸡腹水综合征发病率增加 ,但 B组高于 A组 ;A、B组肉鸡 m PAP从 5周龄起高于 C组 (P<0 .0 5 ) ;A、B组右心全心比的升高分别在 6、5周龄时出现 ,B组右心全心比的升高比 A组提前 1周 ;B组肉鸡血浆 NO水平低于 A组 ,差异不显著 ;A、B组肉鸡血浆 ET- 1水平高于 C组 (P<0 .0 5 ) ;在 6周龄时 ,A组肉鸡心率明显降低 ,B组肉鸡心率明显升高。

英、拉、汉树木名称电子词典TreeName的研制

英、拉、汉树木名称电子词典第1版(TreeName1 0)具有树种的英文、拉丁文和中文名称的相互翻译查询功能。软件包含了1 5万余条英、拉、中树木名称词条,能够进行快速有效的检索查询,为工作提供极大的帮助。整个软件采用基于对话框模式的查询界面和基于文件系统的数据库作为整个查询系统的框架。本系统在设计中采用了比较灵活的功能模块设计,利于软件的更新。与印刷版的各种词典相比,电子词典系统具有无法比拟的优点,它能够及时进行修改、补充,使系统不断得到完善,及时根据用户的反馈信息进行改进,有利于软件质量的提高和功能的完善。

空间网络的标度性质对Naming Game演化行为的影响

鉴于社会网络结构对于信息传播、共识形成等社会行为的重要影响,在有限能量约束条件下,通过添加距离服从幂律分布的长程连边,构造出具有标度性质的空间网络。在此空间网络上,讨论了引入无意收听机制的Naming Game模型的演化行为。研究发现,存在一个最优的幂指数,使得该空间网络上的Naming Game模型收敛时间最短,当能量约束足够大时,这一最优幂指数趋于1.5附近。本研究说明,社会关系网络中的空间性质对于社会集体认同的形成有很大的影响。

L-NAME对实验性自身免疫性心肌炎的保护作用

目的观察NG-硝基-L-精氨酸甲酯(L-NAME)对实验性自身免疫性心肌炎(EAM)Lewis大鼠模型的治疗效果,并探索可能的治疗机理。方法 20只Lewis大鼠建立EAM动物模型:双足底注射心肌C蛋白片段和完全弗氏辅佐剂的油状混合物,腹腔注射百日咳毒素。大鼠随机等分为治疗组和模型对照组,每组各10只。治疗组腹腔注射5 mg·kg-1·d-1L-NAME,从免疫注射术后第1天开始连续20 d,1次/d。对照组相同时间内给予相同剂量生理盐水腹腔注射。治疗结束后第1天处死动物,心脏取材,进行系列检测。其中组织病理学石蜡切片苏木素-伊红(HE)染色检测心肌炎症分级,免疫组织化学染色检测T淋巴细胞浸润,天狼星红染色检测心肌胶原纤维含量,硝酸还原酶法检测NO水平,明胶酶谱法检测胶原酶活性。结果与对照组比较,L-NAME治疗组心肌炎症级别下降[(3.42±0.31)vs(2.51±0.22),P<0.01]、T淋巴细胞浸润数目减少[(28.2±4.6)vs(13.2±1.9),P<0.01]、心肌间质纤维化级别下降[(2.33±0.26)vs(1.14±0.17),P<0.01]、血清NO水平降低[(68.34±8.61)μmol/L vs(45.71±6.53)μmol/L,P<0.01],明胶酶活性降低[(254 526±4 729)vs(184 712±3 869),P<0.01]。结论 L-NAME抑制EAM病理发展过程,其机制可能与通过降低NO水平和明胶酶活性,从而降低心肌炎症细胞浸润,延缓心肌间质纤维化有关。