Incorporating Linguistic Rules in Statistical Chinese Language Model for Pinyin-to-character Conversion

An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods such as MI-based rule evaluating, weighted rule quantification and element-based n-gram probability approximation are presented. Dynamic Viterbi algorithm is adopted to search the best path in lattice. To strengthen the model, transformation-based error-driven rules learning is adopted. Applying proposed model to Chinese Pinyin-to-character conversion, high performance has been achieved in accuracy, flexibility and robustness simultaneously. Tests show correct rate achieves 94.81% instead of 90.53% using bi-gram Markov model alone. Many long-distance dependency and recursion in language can be processed effectively.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

A Bit Progress on Word-Based Language Model

A good language model is essential to a postprocessing algorithm for recognition systems. In the past, researchers have presented various language models, such as character based language models, word based language model, syntactical rules language model, hybrid models, etc . The word N gram model is by far an effective and efficient model, but one has to address the problem of data sparseness in establishing the model. Katz and Kneser et al. respectively presented effective remedies to solve this challenging problem. In this study, we proposed an improvement to their methods by incorporating Chinese language specific information or Chinese word class information into the system.

Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

Discussion of Online Teaching Strategies for Teaching Elementary Chinese Characters to Foreigners Based on Error Analyses

The expanding role of the Chinese language in international communications has become increasingly prominent as China’s comprehensive national power continues to grow,leading to a significant rise in the number of Chinese language learners.Since online teaching is not limited by time and space,its application is widespread.For beginners in the Chinese language,the Chinese characters are both a priority and a challenge.The“Chinese Character Classification,”also known as the“Six Writings,”is the earliest systematic theory of Chinese character structures,and teaching Chinese characters in categories based on the“Chinese Character Classification”is a method that fits the cognition of beginners.In order to teach Chinese characters in a targeted approach,based on the collection and analysis of the common errors of Chinese characters among beginners,(1)this paper proposes that(a)the intuitive method can be applied to teach pictographic characters,indicative characters,and associative compound characters in online teaching;(b)the inductive-deductive method of“basic characters to new characters”can be applied for the teaching of pictophonetic characters and associative compound characters;(c)the learning of character patterns should be approached in a whole-part-whole process,while importance should be attached to the suggestion of the frequency effect with a view to facilitating the online learning of Chinese characters for beginners.The aim of this paper is to provide some practical implications for the online teaching of Chinese characters to foreigners.

Character Development in a Multilingual International School and Its Use of Self-Assessment Tools

This article describes a multiyear initiative of a multilingual multicultural international school that has come to adopt and internalize character development as part of its identity.That is,character education has been treated as a central tenet and core value that permeates the school and binds the community.It has not been regarded as a supplemental or enhancement project,but rather integral to the general educational program.Built from a principled framework with sound theoretical backing,the infusion of character education at this international school has resulted in the crafting of new standards and the introduction of teacher and student self-assessment tools.In that vein,in this article,we share how the school has come to embrace character development and has forged personalized ways for stakeholders,including teachers and multilingual learners,to engage in improving teaching and learning.

Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.

A Brief Case Analysis about Language and Gender

Language and gender is an important topic in sociolinguistic studies. This paper aims to analyze the differences between male language and female language of"Friends"on the basis of understanding the relative theories and studies about language and gender.

A Critical Analysis of DCI(Delayed Character Introduction)System in TCFL

Chinese characters learning is one of the top challenges for novice non-Chinese speaking learners.Comparison betweenDCI(Delayed Character Introduction)and ICI(Immediate Character Introduction)is offered.Further,both affirmative and negativediscussion is presented from the perspectives of feasibility,target language environment,pinyin dependence,and compressiveteaching time.DCI is considered more suitable for novice Chinese learners especially adults,and is more a theoretical suggestionthan a practical pedagogy.

电打汉字开路,笔写汉字跟进——革新国际中文教育中的汉字教学模式

汉语和英语作为外语教学有相同之处,更有相异之处。那相异之处突出体现在中国人学英语,一般来说,口语学习与书面语的学习几乎是同步的,从口语进入书面语学习无需在文字学习上下很大功夫,花很多时间;可是英语区学生学习汉语,其口语学习与书面语的学习无法同步,他们从口语学习进入书面学习,必须在汉字学习上再要下功夫、花很多时间,过好“汉字关”。汉语老师如何帮助学生过好“汉字关”?在如今的智能时代需要考虑革新汉字教学模式,采用“电打汉字”的汉字教学新模式;其具体实施策略是“电打汉字开路,笔写汉字跟进”。如何“电打汉字开路,笔写汉字跟进”?文章提供了具体的教学措施。

汉字词进入朝鲜语的适应性

朝鲜语在与汉语的长期接触中吸收了大量借词,形成了今日的汉字词。朝鲜语中,汉字词的使用十分常见,尤其是在一些专业领域。汉字词在朝鲜语中的适应性很强。汉字词之所以能够适应朝鲜语语言环境实现本语化,与其生存发展的生态环境及自身特性有密切关联。在朝鲜语的发展过程中,汉字词的使用有两次大爆发,第一次是约7~15世纪统一新罗时期至朝鲜王朝前期,第二次是1876~1910年开化时期,这两个时期为汉字词的快速发展提供了良好的生存土壤。汉字词的特性有四:一是表达精细、容易理解;二是构词能力强、能产性高;三是竞争力强;四是影响力大,甚至可以影响到语法体系。

锡伯族语文和满族语文的关系和差异

锡伯族与满族在历史发展中,有过密切的接触与交流。但因锡伯族多次迁徙,与满族的语言接触也随之发生变化。文章依据语言发展的基本规律,论述了锡伯族与满族语言文字之间的亲近关系,并通过考察锡伯口语与盛京满语方言之间在词汇、语言习惯和语法结构等方面存在的诸多差异,解释了锡伯口语的历史传承性,以及锡伯口语与满语的本质差异。在文字方面,则详细阐述了锡伯文是在满文基础上衍生的,并且正处在不断创新、完善和发展的进程之中。

从汉英语作为外语教学的差异试议智能时代的汉语教学

汉语和英语作为外语教学的相异之处突出体现在:中国人学英语,口语与书面语的学习几乎是同步的;而英语区学生学习汉语,口语与书面语的学习无法同步,从口语学习进入书面语学习,必须在汉字学习上再下功夫、花时间,过好汉字关。智能时代,可以用拼音击键或语音输出汉字,这为破除“汉语难学”的迷信带来机遇。国外学者使用“电写为主,笔写为辅”教学模式,取得了良好的教学效果。本文进一步提出“电打汉字开路,笔写汉字跟进”的新思路:在汉语学习的开始阶段,先教授、引导学生“电打汉字”,让学生破除对汉字学习的畏难情绪;再在教学过程中适度穿插“笔写汉字”的练习,引导学生逐步由“电打”过渡到“笔写”,因为“笔写”有助于对汉字的记忆、体会和认识,确保学好汉语书面语。

从多音字“宁”姓氏用法的收录谈规范型工具书与语言规范

工具书中多音字姓氏用法的收录,一直是广大读者非常关注的问题,也是辞书编纂修订工作的难点之一。“宁”是现代常见的姓氏,由于“宁”是多音字,有níng和nìng两个声调不同的读音,并且与姓氏用字“甯nìng”存在比较复杂的关系,目前常见的工具书,包括专门的姓氏辞典,在收录“宁níng”“宁nìng”“甯nìng”姓氏时存在分歧,有的工具书前后版本对“宁”作为姓氏的收录也有调整。本文梳理目前常见工具书的收录情况,从“寜”“甯”“宁”的历史渊源、相关语言规范和应用实际等方面分析收录差异,进而阐述了现代汉语规范型工具书如何落实语言规范、反哺规范的问题。

百年回眸:延安时期中国共产党领导的语文运动

延安时期,中国共产党领导开展了两场影响深远的语文运动。一是新文字运动,一是语言大众化运动,都取得了举世瞩目的成就。延安时期的新文字运动,为更科学合理、更符合中国国情的文字改革做出了宝贵的探索。语言大众化运动以人民大众喜闻乐见的新鲜活泼语言作为民族语言资源来建立起具有中国作风和中国气派的语言风格,最终确立了大众语与现代汉语的话语范式,对整个20世纪的现代汉语走向产生了深远影响。

从社会语言学视角比较《京华烟云》两个中译本的人物语言特色

《京华烟云》是林语堂先生用英文写成、体现清末民初北京风俗的小说,小说人物众多、个性鲜明。因此译者将其翻译回中文时,应注意人物语言符合特定的地域、时代以及社会背景等。本文通过社会语言学视角,从地域性方言、时域性方言及社会性方言三个维度,比较了《京华烟云》两个中文译本的人物语言特色。研究发现,郁飞译本更强调人物语言在上述三个维度的还原,更好地凸显了中国文化特色。

中华文化“走出去”背景下散文英译探微——基于翻译写作学的视角

在倡导中华文化“走出去”的背景下,承载中华传统文化的优秀文学作品的外译成为了学术界关注的焦点。翻译写作学是一种旨在指导译者如何译出佳作的翻译研究理论。在梳理中国散文英译研究现状的基础上,翻译写作学理论可以为散文英译研究提供新的研究视角,助推中华文化“走出去”,由此印证了翻译写作学对指导汉英翻译、提高译文质量具有积极的意义,翻译写作学的研究视野也由此得到完善。

红色文化融入高校外语教学的研究

红色文化为实现中华民族的伟大复兴提供了强大的精神力量,而在高校外语教学中融入红色文化,不仅可以帮助学生树立文化自信,而且能有效传承并弘扬红色文化。但在高校外语教学中仍存在教师对红色文化资源搜集不全面、高校学生对红色文化价值观的理解还不够深入、在教学中缺乏与红色文化的深度融合等问题。高校应增强教师的红色文化素质,在教学中进行红色文化的渗透,开展红色文化实践等,以推动红色文化的传承与创新。