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Based on consideration of learner needs for expanding vocabulary and the complexity of educational content, this paper introduces a model aimed at facilitating English vocabulary learning.

By measuring a set of effective variables regarding simplicity of English sentences, a ranking algorithm is presented in the proposed model. According to this ranking, the simplest sentence in the recommender system (RS) is selected and recommended to the user. Furthermore, Pearson correlation coefficient was used for checking the degree of correlation among the respective parameters on sentence simplicity. For evaluating the efficiency of the recommended algorithm, a prototype was designed by programming using Embarcadero Delphi XE2.

The results of the study indicated that the correlation among the parameters of word frequency, sentence length and average dependency distance were 0.723, 0.683 and 0.589, respectively. The computed final score is considered to be more accurate.

The application of RS in language learning and education sheds light on the theoretical validity of system thinking by highlighting its key features: its multidisciplinary nature, complexity, dynamicity and the interdependence and relation of micro and macro levels in a system.

The proposed method has significant pedagogical implications; it can be used by second language teachers and learners for checking the degree of complexity/learnability of discourse and text.

This paper proposes an alternate model with a significantly higher speed for computing final sentence score.

This paper aims to examine and critique the accounting literature's dominant readability formula, the Flesch formula. Furthermore, the paper sets out to propose refinement and augmentation to the formula with a view to expanding its applicability and relevance to researchers' attempts at better understanding and critiquing the effectiveness of accounting communications. This aim extends to setting a more robust foundation for informing policymakers' and practitioners' interest in implementing more effective communications with their target stakeholders.

The paper offers an historically informed methodological critique of the current articulation and application of the Flesch formula, both generally and in accounting research. This critique forms the basis for developing proposed revisions and supplementary measures to augment Flesch's coverage. These are presented with sample empirics.

Illustrative examples suggest that it is feasible and desirable to apply a revised formula that reduces Flesch's misplaced emphasis on word length by respecifying its sentence length variable, a probable cause of low readability. A reader attribute score further enhances the formula by integrating the considerable impact of readers' attributes on readability and accounting communication effectiveness. Supplementary measures, comprising non‐narrative communications dimensions, are introduced as a foundation for further research.

The paper provides not only critique but also refinement and augmentation of the much used Flesch readability formula for accounting communications research. It offers a first stage approach to encompassing potentially important communication elements such as readers' attributes, tables, graphs and headings, to date critiqued as potentially important but left unattended by accounting researchers. This offers the prospect of extending Flesch's application to contemporary accounting communications issues and questions.

The purpose of this paper is to measure the understandability of the illustrations provided by the International Federation of Accountants in terms of the structural features of international auditors’ reports with modified opinions.

Measurement of the legibility of reports illustrated by the revised ISA 705 and ISA 706. This paper discusses the compliance level of modified auditors’ reports with the linguistic rules.

It was found that the standardized illustrations of modified reports are not fully understandable by users of financial statements. The illustrations of modified auditors’ reports are not compliant with several linguistic rules.

This paper provides new original investigation about the linguistic features of illustrations provided by the ISA 705 and ISA 706. This paper discusses the level of unintelligibility of standardized auditors’ reports and the implications for stakeholders.

The purpose of this study is to investigate the impact of board secretaries’ characteristics on annual report readability using an original method that evaluates the readability of Chinese characters.

The authors manually collect board secretaries’ characteristics from the China Securities Market and Accounting Research database and obtain annual reports from the China Information website. Ordinary least square regression is applied to evaluate the impact, and then robustness tests and additional regression analyses are conducted.

Board secretaries’ legal-professional expertise, international expertise and role duality improve annual report readability. However, their political connections are negatively associated with it. The effect of expertise (role duality) is more pronounced for firms with lower ex ante litigation risk (board secretaries with equity holdings). Furthermore, higher readability increases the compensation of board secretaries, whereas lower readability increases their turnover. Finally, annual report readability is positively related to firm performance.

The authors only investigate listed firms in China from 2007 to 2017 because of the difficulties of obtaining data and text mining.

The authors provide managerial insights for regulators aiming to establish an effective governance mechanism with Chinese characteristics. First, certain requirements for board secretaries’ expertise can improve annual report readability. Further, firms can consider appointing board members or senior executives as board secretaries to enhance disclosure quality.

To the best of the authors’ knowledge, this study is the first to verify the effect of board secretaries’ characteristics on disclosure quality, especially annual report readability. Moreover, this study proposes a novel measure of annual report readability for Chinese texts.

Government policy mandates information disclosure in financial communications to protect consumer welfare. Unfortunately, low readability can hamper information disclosures’ meaningful benefits to financial decision making. Thus, this experiment tests the product evaluation and decision satisfaction of Korean consumers with less or more subjective knowledge and with or without personal finance education.

A between-subjects experiment examined responses of a nationally representative sample of 400 Korean consumers toward a Korean-language credit card advertisement.

Financial knowledge improves financial product evaluation and decision satisfaction. More readable disclosures improved evaluation and satisfaction among less knowledgeable consumers. Less readable disclosures did not. Consumers without financial education exhibited lower evaluations and decision satisfaction regardless of readability. More knowledgeable consumers and those with financial education performed equally well regardless of disclosure readability.

Financial service providers seeking more accurate evaluations and better decision satisfaction among their customers should use easier-to-read disclosures when targeting consumers with less prior financial knowledge.

One-size-fits-all financial communications are unlikely to achieve public policy or consumer well-being goals. Government-mandated information should be complemented by augmenting financial knowledge and providing personal finance training.

Although almost a quarter of the world’s population lives in East Asia, this is the first examination of readability in disclosures written in East Asian characters rather than a Western alphabet. Previous readability research on Asian-originating financial disclosures has been conducted on English-language texts. This study extends knowledge of readability effects to growing East Asian markets.

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

Research identifies nearly 73 million adult Americans as illiterate. Analysis of social, economic and demographic trends indicates that the situation will worsen before it improves. Marketing communications copy prepared at an eighth grade reading level or above may not be comprehended by as much as one‐third of the population, resulting in a severe loss of potential sales and excessive advertising expense. Marketing research to date focusses on the impact of message, source, and channel variables on consumer behavior. However, the current “illiteracy crisis” argues that we reform our thinking ti consider how consumer literacy should influence our message, source, and channel determinations. Key tasks for marketers include evaluating the clarity, readability and specificity of promotional materials; pretesting the marketing communication on a sample of the target audience; and carefully assessing all options available for source and channel selection.

Individuals with severe speech and language impairment as a result of chronic severe Broca's aphasia may rely on non‐speech communication aids to augment or replace speech. These aids include speech‐generating devices and graphic symbol software programs that produce synthetic speech upon activation. Previous research has indicated that individuals with chronic severe Broca's aphasia are able to identify, manipulate, and combine graphic symbols to produce simple phrases and sentences. The primary aim of this study is to evaluate the ability of three individuals with chronic severe Broca's aphasia to produce graphic symbol sentences of varying levels of complexity using a speech generating device. A single‐subject multiple‐baseline design across behaviours replicated across three participants was used to assess the effect of AAC intervention on the production of sentences using graphic symbols. Findings indicated that individuals with chronic severe Broca's aphasia were able to combine graphic symbols to produce sentences of varying levels of complexity. The results of this study suggest that technologically‐based AAC intervention approaches can be effective in facilitating communication for individuals with chronic severe Broca's aphasia. The overall findings are discussed in terms of clinical and public policy implications.

Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.