Characteristics Of Text Research Articles

Синтаксические способы организации профессионального дискурса активного воздействия

The investigation focuses on the study of linguistic methods of impact in professional discourse. The research outlines the grounds for professional discourse classification and highlights textual and communicative characteristics of professional discourse. The objective of the study is to determine the range of language means that may help to create communicative impact within the framework of professional interaction. The contribution describes one of the stages of professional discourse investigation in order to demonstrate the results of active effect discourse analysis on the syntactic level. The article highlights the features of the implementation of pragmatic strategies of defense statements. This type of legal discourse is considered in comparison with other ways of implementation of legal discourse, as well as with other types of active effect discourse. The study has demonstrated that when choosing quantitative and qualitative parameters of syntactic means used in legal texts their authors should take into account genre characteristics of these texts. Expressive syntactic means are intensively used in judicial defense statements in order to intensify the impact they produce on the audience. The research has revealed that among expressive syntactic means most frequently use in defense statements are the following: parallelism, anaphora, asyndeton, climax, and parenthetical insertions. The publication also aims to demonstrate the potential of the applied methodology in terms of further investigation of various types of professional discourse; such investigation may allow to outline the most efficient means of speech impact in professional discourse, and therefore, to increase the effectiveness of professional interaction.

Textual characteristics of corporate sustainability disclosure and corporate sustainability performance: evidence from Australia

Purpose This study aims to analyze whether various textual characteristics in corporate sustainability disclosure associate with corporate sustainability performance in Australia, pertaining to tones of language and readability. The voluntary disclosure theory and legitimacy theory are used to formulate the study hypothesis. Design/methodology/approach Using data from Australian listed firms (2002–2016), four textual characteristics are examined: tone of optimism, tone of certainty, tone of clarity and readability. Corporate sustainability performance is measured by Thomson Reuters Asset4 ratings. Different strategies are adopted to mitigate endogeneity concerns. Findings The authors found that there is a positive relationship between the textual characteristics of sustainability disclosure and sustainability performance. Specifically, firms with better performance communicate in an optimistic, certain, clear and more readable manner. Practical implications The results suggest that Australia’s voluntary reporting status does not induce a combination of poor performance and positive disclosure. This paper should be of interest to investors and other stakeholders and also informs regulatory policy on sustainability disclosure in Australia. Originality/value The authors contribute to the sustainability disclosure literature using computer-based textual analysis to explore whether firms reveal their sustainability performance by “how things are said” (i.e. textual characteristics) in sustainability disclosure. As far as the authors could ascertain, they are the first to investigate textual characteristics of sustainability disclosure in Australia.

Multispectral and SAR Image Fusion Based on Laplacian Pyramid and Sparse Representation

Complementary information from multi-sensors can be combined to improve the availability and reliability of stand-alone data. Typically, multispectral (MS) images contain plentiful spectral information of the Earth’s surface that is beneficial for identifying land cover types, while synthetic aperture radar (SAR) images can provide abundant information on the texture and structure of target objects. Therefore, this paper presents a fusion framework to integrate the information from MS and SAR images based on the Laplacian pyramid (LP) and sparse representation (SR) theory. LP is performed to decompose both the multispectral and SAR images into high-frequency components and low-frequency components, so that different processing strategies can be applied to multi-scale information. Low-frequency components are merged based on SR theory, whereas high-frequency components are combined based on a certain activity-level measurement, identifying salient features. Finally, LP reconstruction is performed to obtain the integrated image. We conduct experiments on several datasets to verify the effectiveness of the proposed method. Both visual interpretation and statistical analyses demonstrate that the proposed method strikes a satisfactory balance between spectral information preservation and the enhancement of spatial and textual characteristics. In addition, a further discussion regarding the adjustability property of the proposed method shows its flexibility for further application scenarios.

English-Chinese Translation Strategies for Product Instructions

The exchange between domestic and overseas products as well as services is increasing owing to the gradual advancement of globalization. In 1993, Gu Rong first proposed the concept of “translating product instructions,” then product instructions became a hot topic in the field of translation research. Based on empirical research, this paper analyzes the textual characteristics and translation strategies for product instructions in a bid to provide reference for translation research [1].

Abnormal disclosure tone, earnings management and earnings quality

PurposeThe authors investigate whether abnormal tone in corporate narrative disclosures is associated with earnings management and earnings quality, in an emerging market context. Based on agency theory and opportunistic/impression management perspective, this study examines whether executives manage disclosure tone to support their opportunistic behavior, when using earnings management.Design/methodology/approachThis study uses a sample of earnings press releases of publicly traded firms in the MENA region during 2014–2019. It employs textual analysis to measure disclosure tone. The authors estimate abnormal disclosure tone after controlling for firm characteristics. Discretionary accruals proxy for earnings management and are estimated using Modified Jones model. Earnings quality is measured using accounting-based and market-based proxies: earnings smoothness, persistence, predictability and value relevance/informativeness.FindingsResults show a positive association between abnormal disclosure tone and earnings management. Additionally, results show that earnings persistence is higher for firms with lower levels of abnormal disclosure tone. Results are sustained for earnings smoothness, but not for predictability and value relevance/informativeness.Research limitations/implicationsResults provide initial evidence of management's use of tone management jointly with earnings management. This adds to prior studies adopting the opportunistic perspective of disclosure tone, through showing that discretionary tone in narrative disclosures can be strategically used by management to influence investors' perceptions.Practical implicationsThe results provide valuable insight to board of directors, auditors and market participants on the possible biases emerging from tone of narrative disclosures in corporate reports. For regulators and standard-setters, results shed light on the need for regulations and rules beyond financial statements, to guide disclosure of narrative information in different corporate reports.Originality/valueThis study contributes to the rare evidence that investigates textual disclosure characteristics to uncover management's opportunistic practices and assess earnings quality. Where majority of studies concentrate on developed markets, this study provides novel evidence of emerging markets by examining the association between abnormal disclosure tone and earnings management/earnings quality. Also, it validates the tone management model proposed by Huang et al. (2014) for capturing tone manipulation.

A Chinese Named Entity Recognition Model of Maintenance Records for Power Primary Equipment Based on Progressive Multitype Feature Fusion

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE‐MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment (namely, main transformer, circuit breaker, and isolating switch) are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE‐MR dataset are implemented, respectively, which show the effectiveness of the proposed method.

Stage Monologue and its Linguistic Features (Textual and Prosodic Analysis)

Spoken language production is considered to be one of the most difficult aspects of teaching a foreign language. It usually involves mastering pronunciation of sounds and intonation. If nowadays many teachers do not worry about the phonetic details of sounds, there is still focus on intonation as it has a great impact on the comprehensibility of the learner’s English. This is a very important issue for future teachers because correctness of pronunciation is one of the goals of any spoken language programme, with students asked to produce quite extended spoken monologues and to follow the requirements of various intonational styles. The aim of this study is to analyse textual and prosodic characteristics of stage monologue – a text produced on a theatre stage or in a film. Analytical methods were applied in order to obtain information about textual features and prosodic stylistic markers such as pitch level, range, tone modifications, loudness, and tempo, and also to develop style-forming factors in stage monologue. Results show that the stage monologues analysed possess all necessary characteristics of a text: informational content, delimitation, continuum, coherence, cohesion and completeness. Further analysis of stage monologue showed that it can be characterised by such specific features as expressiveness, normativeness, effectiveness, and conversational character. Stage monologues also possess all necessary prosodic markers. Certain style-forming factors of stage monologue were also developed in this study, including delimitation, accentuation of key words, thematic centres and expressively prominent centres, type of composition scheme, and theme. These results will be of significant pedagogical value to students who intend to become English teachers, and to teachers involved in linguistics research.

Hybrid machine learning approach for Arabic medical web page credibility assessment.

For many people, the Internet is their primary source of knowledge in today's modern world. Internet users frequently seek health-related information in order to better understand a health problem, seek guidance, or diagnose symptoms. Unfortunately, most of this information is inaccurate or unreliable, making it difficult for regular users to discern reliable sources of information. To determine online source reliability, specific knowledge and domain expertise are necessary. Researchers in health informatics studied a number of linguistic and non-linguistic indicators to assist ordinary individuals in judging medical web page credibility. This study proposes a method that automates the process of assessing the reliability of online medical sites based on textual and non-textual characteristics. To evaluate the proposed approach, we developed a real-world dataset of Arabic web pages that provide medical information. This dataset is the first Arabic medical web page dataset for content credibility evaluation. The hybrid approach was assessed using multiple machine learning and deep learning algorithms on the dataset, providing an accuracy and F1-score of 79% and 77%, respectively. We also identify the most observable patterns that help evaluate or detect unreliable web pages written in Arabic.

On the Textual Characteristics of Legal English Texts —Taking the Personality Rights of the Civil Code as an Example

On the Textual Characteristics of Legal English Texts —Taking the Personality Rights of the Civil Code as an Example

Regional language Speech Emotion Detection using Deep Neural Network

Speaking is the most basic and efficient mode of human contact. Emotions assist people in communicating and understanding others’ viewpoints by transmitting sentiments and providing feedback.The basic objective of speech emotion recognition is to enable computers to comprehend human emotional states such as happiness, fury, and disdain through voice cues. Extensive Effective Method Coefficients of Mel cepstral frequency have been proposed for this problem. The characteristics of Mel frequency ceptral coefficients(MFCC) and the audio based textual characteristics are extracted from the audio characteristics and the hybrid textural framework characteristics of the video are extracted. Voice emotion recognition is used in a variety of applications such as voice monitoring, online learning, clinical investigations, deception detection, entertainment, computer games, and call centres.

Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

Corporate Disclosures in Pandemic Times. The Annual and Interim Reports Case

ABSTRACT The paper examines if COVID-19 crisis has brought changes in companies’ approach on corporate reporting, with focus on annual reports. The research method is based on text mining techniques in order to build measures of readability and tone of uncertainty of annual reports, given the information published by the companies listed on four stock exchanges from Europe, namely the Bucharest Stock Exchange, ATHEX Stock Exchange, IBEX-35, and WIG-20 between 2017–2020. Findings emphasize, through text mining, multivariate analysis, and topic modeling, that the analyzed reports are less extensive in times of pandemic and tend to become more generic. Among firms’ financial performance metrics considered in our models, we found that there is a significant association only between annual reports textual characteristics and respectively, firm size, price earnings ratio and accruals reported. We prove as well significant stock exchange effects and industry effects. Our results show a slight decrease in annual reports readability, while the tone of uncertainty is more prominent within firms listed on less mature stock exchanges.

Defend or remain quiet? Tax avoidance and the textual characteristics of the MD&A in annual reports

Firms engage in tax avoidance (TA) for two reasons: to save funds or to manage agency conflicts. We hypothesize that if the motivation stems from agency conflicts, managers are likely to manipulate the text of annual reports to mask TA to preserve their private interests. In contrast, where firms engage in TA to save money, managers are likely to refrain from comment in the annual report because they have no incentives to defend their TA activities. Using a sample of Chinese firms and a textual analysis of the management discussion and analysis (MD&A) section of annual reports, we find robust evidence to support the agency conflict motives of TA. Specifically, we document that when a firm increases its TA, its MD&A is similar to that of its peers and to its previous annual report, and contains more tax-related key words. Further analysis shows that the impact of TA on tax-defensive behaviors is more salient for firms with small board size, less transparency, or low financial constraints. Last, we find that, conditional on a firm's TA activities, text manipulation in an MD&A alleviates the adverse impact of the TA on immediate- and short-term stock returns.

Do firms strategically respond to retail investors on the online interactive information disclosure platform?

Based on the official online interactive question and answer (Q&A) platform in China, we examine whether firms strategically change their response characteristics according to the tone of retail investors’ questions and how firms’ response choices affect stock market reactions to investors’ questions. Our findings show that firms respond less directly but more optimistically when the sentiment embedded in investors’ questions is more negative, consistent with the notions that firms are strategically responding to retail investors in the process of online interactive information disclosure. Additional analysis suggests that both the information embedded in the response and the textual characteristics of the response play an important role in stock valuation.

Differential effects of online review characteristics on innovators and imitators in new product adoption

The effects of online user reviews on new product adoption are important in managing interactive marketing communications. However, such effects may vary, depending on the nature of the review characteristics and consumers. In this paper, we empirically investigate the differential effects of online review characteristics on new product adoption for two types of consumers (innovators and imitators) and how such effects may be altered by different dimensions of review characteristics. Using a data set collected from Amazon, we find that the differential effects exist, but are moderated by the dimensions of review characteristics: the numerical review characteristics (i.e. review volume, review rating, and rating variation) have larger effects on imitators than on innovators, but the review textual characteristics (i.e. review emotional content) show the opposite effects. We further discuss the managerial implications of our research findings.

Why Medieval Allegory?

Why Medieval Allegory?

Linguistic changes in the transition from summaries to abstracts: The case of the Journal of Experimental Medicine

Linguistic changes in the transition from summaries to abstracts: The case of the <scp> <i>Journal of Experimental Medicine</i> </scp>

P.Cair.EgLib. 513: A Certificate for a Ziyāra to the Prophet’s Tomb in Medina (16th century)

This article presents a hitherto unpublished document preserved at Dār al‑Kutub al‑Miṣriyya (Cairo) under inventory number P.Cair.EgLib.inv. 513. The document extols the holy city of Medina and contains a diagrammatic representation of the Prophet’s mosque (al‑masǧid al‑nabawī) comparable to other illustrated scrolls dated to the sixteenth century. The material, textual and iconographic characteristics of this document raise the possibility that it could be the draft of a ziyāra certificate attesting to a pious visit to the tomb of Muḥammad in Medina.

Community Benefit Report Spending and Content Analysis

There is substantial debate regarding the community benefits provided by nonprofit hospitals in exchange for tax-exempt status at the federal and state levels. Despite the controversy surrounding this topic, research into community benefit spending is a relatively new area of academic research. This study examines community benefit reports from nonprofit hospitals in the California Office of Statewide Health Planning and Development (OSHPD) database. We employ text-based content analysis to determine how the language used in current-year reports impacts community benefit spending in the following year. Our study contributes to the literature because it is the first paper that conducts text-based content analysis of community benefit reports using the following five textual characteristics: length, boilerplate, fog, specificity, and tone. We find that the length, specificity, and tone of the reports significantly impact community benefit spending.

The Experimental Gothic

Abstract This essay proposes that the crucial movements of the historical avant-garde looked to the first wave of Gothic literature (1760s–1820s) in developing their respective variants of experimental prose. To date, the linguistic and textual characteristics (non-mimesis, ineffability) of the literary mode here called Experimental Gothic have not been comprehensively investigated, neither in Avant-Garde nor Gothic Studies. The proposed poetics of the Experimental Gothic indicates that the early avant-gardes did not straightforwardly recycle Gothic material but rather wove the praxis of contemporary theories of representation into their prosaic exploits, which were immersed in the imaginary, supernatural and irrational. The linguistic features of recognised works of avant-garde prose by luminaries such as Carl Einstein, Hugo Ball and Julien Gracq reveal the Experimental Gothic to be a language project spawned from anarchist backgrounds, which leads readers to reject their naïve belief in conventional representation in order to gain a renewed sense of reality.