Average Word Length Research Articles

Quality and Readability Analysis of Information on Cleft Lip and Palate: Sample of Online Resources in Turkish

Aim: Cleft lip and palate is one of the most common congenital craniofacial conditions with follow-up of many different professionals usually beginning at birth and continuing throughout life. Internet resources can influence people's decisions on health-related issues. The purpose of this study was to examine the information quality, verifiability, standard compliance, and readability of websites that offer information on cleft lip and palate. Method: The first 100 websites accessed through "http://www.google.com.tr/" using the keyword "cleft lip and palate (“dudak damak yarıkları” in Turkish)" were evaluated and 64 websites were included in the analysis. The information quality, conformity to standards, and verifiability of the websites were scored according to HONcode criteria (0=lowest; 16=highest); Ateşman (0–29=very difficult; 90–100=very easy) and Çetinkaya (0–34=disabled level; 51+=independent level) readability indices were applied. Results: The mean HONcode score of the websites was 4.79±2.07 (min=0; max=9). The average word length was 2.77±0.12 (min=2.44; max=3.13) and the average sentence length was 11.46±2.44 (min=5.4; max=19.1). The mean of Ateşman readability analysis was 57.57±8.92 (min=32.9; max=77.4) and the mean of Çetinkaya readability analysis was 57.93±3.27 (min=49.72; max=64.64). Conclusion: The results indicate that the web pages have limited standards compliance, verifiability, and information quality. The Ateşman index places readability at a medium difficulty level, while the Çetinkaya index places it at an independent level. It is emphasized that content producers should pay attention to quality standards while developing the reading material on websites related to cleft lip and palate and pay attention to readability features so that visitors can easily access information.

Identifying ChatGPT-Written Patient Education Materials Using Text Analysis and Readability.

Artificial intelligence (AI)-based text generators such as Chat Generative Pre-Trained Transformer (ChatGPT) have come into the forefront of modern medicine. Given the similarity between AI-generated and human-composed text, tools need to be developed to quickly differentiate the two. Previous work has shown that simple grammatical analysis can reliably differentiate AI-generated text from human-written text. In this study, ChatGPT was used to generate 25 articles related to obstetric topics similar to those made by the American College of Obstetrics and Gynecology (ACOG). All articles were geared towards patient education. These AI-generated articles were then analyzed for their readability and grammar using validated scoring systems and compared to real articles from ACOG. Characteristics of the 25 AI-generated articles included fewer overall characters than original articles (mean 3,066 vs. 7,426; p < 0.0001), a greater average word length (mean 5.3 vs. 4.8; p < 0.0001), and a lower Flesch-Kincaid score (mean 46 vs. 59; p < 0.0001). With this knowledge, a new scoring system was develop to score articles based on their Flesch-Kincaid readability score, number of total characters, and average word length. This novel scoring system was tested on 17 new AI-generated articles related to obstetrics and 7 articles from ACOG, and was able to differentiate between AI-generated articles and human-written articles with a sensitivity of 94.1% and specificity of 100% (Area Under the Curve [AUC] 0.99). As ChatGPT is more widely integrated into medicine, it will be important for health care stakeholders to have tools to separate originally written documents from those generated by AI. While more robust analyses may be required to determine the authenticity of articles written by complex AI technology in the future, simple grammatical analysis can accurately characterize current AI-generated texts with a high degree of sensitivity and specificity. · More tools are needed to identify AI-generated text in obstetrics, for both doctors and patients.. · Grammatical analysis is quick and easily done.. · Grammatical analysis is a feasible and accurate way to identify AI-generated text..

A Corpus-based Study on Lexical Features in the English Version of Report on the Work of the Government (2003-2023)

In the context of globalization, China’s voice has been recognized by more and more countries. As the most important political document of China, The Report on the Work of the Government is the main content of translation research of political papers. Its translation provides a significant window for people worldwide to understand China comprehensively. Based on the corpus method, this paper analyzes the lexical features of the English version of Report on the Work of the Government (2003-2023) in terms of the average word length, the type-token ratio, keyword and high-frequency words to explore the image of China constructed, and offer experience for the translations of other publicity materials.

Linguistic Characteristics of Research Article Titles in National and International ELT Journals

Researchers have extensively studied the linguistic characteristics of research article (RA) titles. However, no research has investigated RA titles viewed from research design types published in national and international English Language Teaching (ELT) journals. Thus, the present study aims to fill this gap by investigating the average word length, syntactic structure, and syntactic structure type based on research design. This study used a comparative mixed method design to analyze 120 RA titles published in 2022-2023. The average length of titles was analyzed by counting the parts of speech of the tiles, the syntactic structures of titles were analyzed using the Framework of Cheng et al. (2012), and syntactic structure types based on research design were analyzed by reading the method section of the articles. Results indicate that the average length of the RA titles was 12 words in national journals and 13 words in international journals. Nominal structure was the most used RA title type in national journals, while compound structure was the most used in international journals. Furthermore, the nominal structure was often used for experimental design in international journals, while it was often used for descriptive study design in national journals. The compound and Verb-ing phrase structures were often used for case study design in both international and national journals. In conclusion, there are important differences and similarities in RA title characteristics between the two groups of ELT journal rankings. The findings contribute to the scholarship on the contrastive aspect of titling practices in English for Publishing Purposes.

Text Value and Linguistic Characterization in Chinese Language Literature Based on Text Mining Techniques

Abstract This study applies text mining techniques to deeply analyze Chinese language and literature’s text value and linguistic features. The study adopts the methods of textual disambiguation, vector space modeling, semantic network and Labeled-LDA model. Taking the novels of Yu Hua and Ge Fei as an example, it reveals the differences between the two writers in linguistic features such as using punctuation, average word length, and sentence discrete degree. The study provides a comprehensive heat score for the novels based on three dimensions: reading base group, reading gain, and reading discussion. The results show that the frequency of period use in Yu Hua’s works is decentralized, while Ge Fei’s works are more concentrated. Ge Fei’s average word length is slightly higher, showing a tendency to use multi-syllabic words. The novel popularity and heat scores conform to a power law distribution, reflecting the Pareto rule that 80% of the popularity is concentrated on 20% of the hot novels. This study provides a new perspective on Chinese language and literature through the application of text mining technology, and its methods and tools can effectively enhance the effectiveness and efficiency of teaching.

A Comprehensive Analysis of Text Value and Linguistic Characteristics of Chinese Language Literature Based on Text Mining Technology

Abstract In this paper, webpage information is extracted by the directed crawling method in data crawling technology so as to obtain a collection of Chinese language literature, which is processed by data cleaning, Chinese word splitting, and de-duplication. Text mining techniques such as machine learning, the LDA model, and semantic networks are used to perform operations such as sentiment analysis, theme extraction, and linguistic association analysis on the acquired text. Based on the mined text, text value assessment and linguistic characterization of literary works written in Chinese are carried out. The text value is quantified and graded using the PMC index model, and the linguistic features of the text, including punctuation, vocabulary, and sentences, are analyzed by constructing a linguistic feature model. Eight of the 10 Chinese literary works selected in this paper, including Alive, have text values at an excellent level. The frequently used punctuation mark in works is the period. The average word length is around 2.75, and the degree of discreteness of sentences is small.

Exploring English Translation Strategies Oriented by Big Data Technology

Abstract English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.

Stylistic Analysis of Chinese Language Literature Based on Text Mining Techniques

Abstract Chinese literary style highlights the distinctive features of different Chinese language literary works, which is of enormous significance to the research in the field of Chinese language literature. In this paper, we use web crawler technology to construct a dataset of 183 Chinese-language literary works obtained from online reading websites, and we use statistical-based segmentation methods, de-duplication, and other methods to preprocess the text data. The conditional cooccurrence matrix represents the acquired texts, and the BRET-AE model extracts text features. Appropriate classifiers are selected for different literary style analysis tasks. This paper selects seven literary works by Mo Yan and Jia Pingwa for empirical analysis of the Chinese language literary style. While Jia Pingwa’s word formation rate is higher than that of Mo Yan’s. Are selected for empirical analysis of Chinese language literary style. The average word length of all of Mo Yan’s works is 1.5115, and the average word length of Jia Pingwa’s 7 works is 1.3995. In addition, the average sentence length of Mo Yan’s works exceeds that of Jia Pingwa’s works, while the word formation rate of Jia Pingwa’s works is higher than that of Mo Yan’s works. Eventually, the clustering degree analysis reveals that Mo Yan’s Red Red Sorghum Family has the lowest clustering degree, while Wine Country has the highest clustering degree, highlighting the literary style transformation of Mo Yan’s works.

A Study on Teacher Feedback and AES Feedback in Chinese College students’ English Writings

Feedback holds significant importance in second language writing instruction, as evidenced by numerous studies indicating its direct or indirect impact on the quality of learners’ written work. Despite this recognition, there has been a dearth of comprehensive research concerning the distinct influences of teacher feedback and Automated Essay Scoring (AES) feedback on the writing quality of learners. Addressing this gap, the present study employs a mixed-method approach, integrating both qualitative and quantitative methodologies. By meticulously examining AES feedback, teacher feedback, writing revision logs, and conducting interviews, this investigation identifies noteworthy differentials in the multidimensional aspects of writing quality attributed to these two distinct feedback modalities. Primarily, within the realm of syntactic complexity, lexical richness, fluency, and accuracy, the cohort exposed to teacher feedback demonstrated notably superior performance relative to their AES feedback counterparts. Secondarily, an assessment of revised text quality revealed compelling insights. The ultimate version of the text, stemming from the AES feedback group’s iterative revisions, exhibited marked enhancements in terms of accuracy, total word count, and average word length. In contrast, the initial and final drafts of the teacher feedback group unveiled discernible disparities in vocabulary intricacy, accuracy, total word count, and average word length. Evidently, while the ultimate version did not witness a significant surge in average word length or total word count, it showcased heightened vocabulary sophistication and enhanced accuracy in relation to the initial draft. This study underscores the value of judiciously deploying these two categories of feedback within the landscape of writing instruction. The nuanced benefits of each feedback type can be strategically harnessed to suit distinct writing contexts, thereby augmenting the caliber of learners’ written compositions.

A lexical network approach to second language development

This study investigates lexical development in second language (L2) learning from the perspective of complex dynamic system theory (CDST) using a complex network method. Based on authentic written output texts from L2 Chinese learners of different proficiency levels and language backgrounds, we successfully differentiate between different proficiency levels using a bi-gram lexical network model at a corpus level. A more in-depth investigation reveals that when compared to traditional lexical complexity indices, such as average word length and hapax legomena percentage (though Guiraud proves to be a robust predictor), the lexical network indices, such as network density and network clusters, provide a more profound understanding of L2 proficiency distinctions and a more precise approximation of the target language. Moreover, our findings illuminate the consistent manifestation of complex network characteristics within L2 Chinese lexical networks across all proficiency levels. Additionally, word association features, encompassing more than just word frequency information, provide comprehensive properties of the interlanguage system, as supported by their information gain values. We argue that studies within the CDST framework should integrate both lexical frequency and lexical network features to gain a comprehensive understanding of L2 lexical development.

Comparative Analysis of Reading Text Readability in Chinese Junior High School English Teaching Textbook Based on Corpus

Readability is one of the important factors for students to consider when choosing a text to read. Based on a self-built corpus of Junior High School textbooks in China, this paper mainly investigates the differences in readability between reading texts in junior high school English textbooks. From vocabulary, sentence, and comprehensive indicators, this study makes quantitative statistics on the factors that affect the readability of English textbook texts to understand the readability of different textbook texts and to explore whether there are differences in the readability of eight textbooks. Finally, the results show significant differences in the readability of the eight editions of English readers. The average word length and new words rate are the highest in Popular Science Press, which reach 4.43 words and 8.40%, respectively. Among the average sentence length and the frequency of compound sentences, the Shandong Education Press has the highest, going 11.36 words and 11.03%, and confirmed that the Shandong Education Press has the highest difficulty. The textbook arranger can take the difficulty of the reading text into account once they have determined the readability of the text. They can then arrange or select a textbook appropriate for students, enabling them to comprehend and master the text more effectively.

Lexical predictors of text complexity: The case of Russian as a foreign language

The article presents results of a comparative analysis of lexical complexity of educational texts in teaching Russian as a foreign language. The corpus size of the study is about 0.5 million words evenly distributed among six levels of language proficiency (A1-C2, Russian National System of Certification Levels of General Proficiency in Russian as a Foreign Language, further – RNSCL). The analysis algorithm is demonstrated based on B2 level texts, for which we estimated the values of the eight complexity predictors using the automatic analyzers RuLex (rulex.kpfu.ru) and RuLingva (rulingva.kpfu.ru): the number of tokens and types, sentence length, word length, lexical diversity (LD), terminological density, readability (MSIS) and frequency. B2 texts demonstrate significant differences in all the parameters, except for the word length. The validated B2 average word length is 2.26 syllables. The increase of lexical diversity from A1 to C2 is insignificant being within the range of 0.3 - 0.5. The complexity growth in RFL texts is accompanied by an increase of terminological density and the readability index. Since the RFL text is an important source of linguocultural information, the research findings may be useful to researchers, developers of educational resources and test materials, and teachers for text selection processes.

PIKTES İÇİN HAZIRLANAN HİKÂYE SERİLERİNİN OKUNABİLİRLİK DÜZEYLERİ

Texts intended for language instruction need to possess specific attributes in order to be prepared in accordance with the learner's level and learning objectives, and to be correctly comprehended by the learner. For this purpose, a set of distinctive criteria has been formulated. Among these, the concept of readability holds prominence. This study aims to determine the readability of open reading materials tailored for children who are learning Turkish as a second language. The study materials encompassed the "Salih Story Series" and the "Aslı Story Series," curated for Turkish language instruction within the scope of the Project for Supporting the Integration of Syrian Children into the Turkish Education System (PIKTES). A total of 20 storybooks, forming two story series, designed for children aged 6 to 12 of Syrian and other foreign nationalities residing in Turkey, at A1 and A2 proficiency levels, were analyzed. This analysis encompassed syllable, word, and sentence lengths; average word and sentence lengths based on readability criteria by Ateşman (1997) and Çetinkaya-Uzun (2010); and readability scores and levels derived from these criteria. The objective was to individually evaluate and ascertain the readability levels of the stories. The research data indicated that the word and sentence counts of the stories exhibited variability according to their sequential arrangement within the series and demonstrated that there was no progressive increase in the readability of the stories, from simpler to more complex, easier to more difficult, or from known to unknown. Following a qualitative approach and designed in accordance with the method of document analysis, this study culminated in proposing recommendations aimed at the development of a formula for assessing the readability of reading texts to be employed in the instruction of Turkish as a second and foreign language. This formula takes into account comprehensibility, and suggestions were put forth regarding measures that can be undertaken to enhance readability.

Fingerspelling Used in Classrooms by Teachers of the Deaf and Hard-of-Hearing.

Studies have shown the benefits of fingerspelling on literacy skills in school-age deaf and hard-of-hearing students. This study is an observation of 20 first- and second-grade classrooms. The classroom observations were coded for fingerspelling event frequency, type, length, and whether it was chained to print. The observations showed that teachers used an average of 54 fingerspelled events during 40-min lessons. Teachers' frequency of fingerspelling was positively related to students' frequency of fingerspelling. The types of words fingerspelled included Vocabulary (nouns, verbs, adjectives, and adverbs), Function (prepositions, articles, and conjunctions), Abbreviations, and Single Letter Names (i.e., manual alphabet). Teachers most frequently fingerspelled Vocabulary words (57.9%, SD = 22.1%) followed by Function words (15%, SD = 11.2%). The average length of Vocabulary and Function words were 4.2 (SD = 0.7) and 2.9 (SD = 1.1) letters, respectively. Teachers chained fingerspelling to print 20% (SD = 10%) of the time. We suggest that teachers could increase and more systematically use fingerspelling in early-elementary classrooms, explicitly bridging the connection between fingerspelling and print given its association with reading.

Coh-Metrix 시스템을 활용한 고등학교 영어 모의고사 읽기 지문의 학년 간 연계성 분석

The main objective of this study was to analyze the continuity of reading passages in high school English mock College Scholastic Ability Test (CAST) exams using Coh-Metrix, a multi-level text analysis tool. To achieve this, the study constructed a corpus of reading passages from the 2022 high school English mock exams and subjected them to a broad range of Coh-Metrix measures. These measures included basic measures (the number of words, the number of sentences, average word length, average sentence length), word frequencies (word frequencies for content words), word features (imageability, concreteness, age of acquisition, familiarity), lexical diversity measures (type-token ratios for content words), personal pronouns (first person pronouns, second person pronouns, third person pronouns), connectives, readability indices (Flesch Reading Ease, Flesch-Kincaid Grade Level), syntactic complexity (noun density scores, the number of words before main verbs), coreference cohesion measures, and semantic cohesion measures. The main results revealed that the continuity of high school English mock tests was well established for average word and sentence length measures, the average number of words, word frequencies, word familiarity, second person pronouns, standard readability indices, and syntactic complexity measures. These findings have implications for the development of reading passages in high school English mock exams.

2015 개정 교육과정 영어I 교과서 듣기활동의 어휘적⋅구문적 특성

Objectives This analysis deals with nine English I textbooks for the 2015 revised curriculum. We investigated the lexical and syntactic features of the text in the listening section activities.&#x0D; Methods To verify lexical features, the quantity of vocabulary words, vocabulary types, and terms with high frequency by publisher were verified, as well as the figures of lexical variety. The average word length and sentence length for each textbook, as well as the high frequency of frequent exposure, were studied in order to analyze the syntactic features. Each textbook's English listening section was compiled as a corpus file for each chapter, and the findings were analyzed by the WordSmith 7.0 tool.&#x0D; Results A total of 26,671 vocabulary words were collected, and 3,569 corpus types were identified. The lexical features revealed that among the nine textbooks in the listening section activity, the textbook with the largest vocabulary contained approximately 4,000 words, while the textbook with the least vocabulary contained about 2,000 words. In nine textbooks, the percentage of verbs and nouns was fairly high. The average length per word was 4.11, and the average length per sentence was 8.23, as a result of the syntactic features.&#x0D; Conclusions It will be important to provide suggested basic vocabulary independently, as well as to alter so that there is no significant variation among textbooks. Furthermore, it implies that teachers may grasp the variations in the textbooks and resolve learning deviations with supplemental materials for learners to use.

The Economy of Czech Exchange in the Slovak Marketplace of Austria After the Fall of Hungary

Abstract Lexical quanta in writing and speech are important indicators of an individual’s social class. This paper analyses the lexical features of the utterances produced by writers/speakers from different social classes in terms of word length based on a representative sample of letters from the Kremnica archive. The study found that writers/ speakers from different social classes showed different average word length in the 2nd half of the 16th century. According to the analysis of the letters, writers/speakers from the upper classes produced utterances with longer words than those from the lower classes. These differences are explained by factors related to the individual’s social class backgrounds and his “right to speech” or “right to be read”. The linguistic economy principle, objective of which is to save more time and energy by conveying more information with less effort is thus far from exhaustive and by no means reflects the whole sociolinguistic reality.

Is it time to reconsider Henry V?

Abstract The average word length of the verse sections of Henry V is greater than that of the prose sections. Contrary to the assumption that word length primarily reflects literary medium (prose–verse), a more extended examination reveals that differences in word length can also reflect differences in authorship. Since 1998, a variety of stylistic analyses have pointed in the same direction: the verse of Henry V is not generally by Shakespeare who wrote the play’s prose.

A Corpus-Based Study of the Translator’s Style of Two Versions of Fu Sheng Liu Ji —Take Pratt &amp; Chiang Su-hui’s and Graham Sander’s Versions as Examples

Fu Sheng Liu Ji is not only an autobiographical prose written by Shen Fu in the Qing Dynasty, but also a love letter to his wife. So far, there have been four English versions of it, translated by Lin Yutang, Shirley M. Black, Leonard Pratt &amp; Chiang Su-hui, and Graham Sanders respectively. Among them, Lin Yutang’s version has been widely studied, and Shirley M. Black’s version is different from the source text in terms of content and layout. Therefore, this research takes the co-translation version of Leonard Pratt &amp; Chiang Su-hui and Graham Sanders’ version as the research object, builds a corpus, and adopts both qualitative and quantitative methods to make a comparison of the translator’s style between the two English versions from different perspectives—vocabulary, sentence, and discourse, analyzing the type/token ratio, lexical density, average word length, number of sentences, average sentence length and readability of the two versions. It is found that the type/token ratio, lexical density, average word length and average sentence length of Pratt &amp; Chiang Su-hui’s version are lower than that of Sanders’ version, and Pratt &amp; Chiang Su-hui’s version is easier to read than Sanders’ version.

A corpus-based investigation of the English translations of Mao Zedong's speeches.

This study adopts a corpus-based approach to examine the linguistic features manifested in the English translations of Mao Zedong's speeches, taking Winston Churchill's speeches (representative of normalized spoken texts) and the spoken texts in BNC Sampler (representative of original spoken texts) as the reference corpora. By investigating the macro- and micro-linguistic features, it is found that the translated Mao's speeches (both direct and inverse translations) differ from normalized spoken texts as well as original spoken texts in three aspects: (i) macro-linguistic features, (ii) the use of personal pronouns, (iii) the use of modal verbs. In terms of macro-linguistic features, the average word length of the English translations is higher than that of normalized spoken texts and that of original spoken texts; the standardized type/token ratio and average sentence length of the English translations are higher than those of original spoken texts, but lower than those of normalized spoken texts. Meanwhile, in terms of the use of personal pronouns, the English translations of Mao's speeches prefer the underuse of the first person singular pronoun I. Furthermore, as far as modal verbs are concerned, the English translations of Mao's speeches prefer the overuse of must and should on the one hand, and the underuse of shall, could, may, and would on the other hand. Therefore, it can be said that the translated Mao's speeches exhibit some particular linguistic features, which can not only be differentiated from normalized spoken texts, but also be distinguished from original spoken texts. They are in a middle position in relation to normalized spoken texts as well as original spoken texts. This in-betweenness not only exhibits Mao's creative and idiosyncratic language use, but also reflects the influence of the language transfer from Chinese into English.