Text Categories Research Articles

STYLISTIC FEATURES OF THE AUTHOR’S NARRATIVE IN THE NOVEL BY M. HADDON «THE CURIOUS INCIDENT OF THE DOG IN THE NIGHT-TIME»

The article is devoted to the study of linguistic graphic means and the author’s narrative in M. Haddon’s novel «The Curious Incident of the Dog in the Night-Time». The subject of the research is a polycode artistic text, the object is the functions of graphic means of depicting reality and the features of the author’s narrative in the novel under study. The relevance of this work is determined by the increasing frequency of the use of polycode elements in works of modern literature, as well as the relatively small number of studies devoted to this topic. The main research method was contextual-interpretative, the essence of which is to study the text in different types of context based on the researcher’s interpretation of text categories and components. Iconic and paragraphemic elements are used in the text. Paragraphemic ones include font, indents, layout of the text on the page, uppercase or lowercase letters. According to the research, paragraphemic elements occur with a high frequency, express the subjective attitude of the character to the statement, indicate the type of connections of the selected text with the plot of the work, give an idea of the place of such a text in the general textual space. The iconic element — diagrams, drawings, pictures — serve as a supplement to the main textual description of the realities of the novel. Iconic elements remain incomprehensible without textual support. They, like paragraphemic elements, serve to attract the attention of the addressee, facilitate the perception of the text, and in some cases serve to further reveal the character of the protagonist. The representation of character language is at a somewhat primitive level, which serves for better implementation of the author’s idea. Polysyndeton, various types of repetition, parallel constructions should be mentioned among the main stylistic devices. All these methods add logic and concreteness to the presentation, which is sometimes grotesque, with the help of which an authentic and very characteristic image of the narrator is created.

A General Linguistic Steganalysis Framework Using Multi-Task Learning

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model. In the proposed framework, convolutional neural networks (CNNs) are utilized as private base models to extract sensitive features for each steganalysis task. Besides, a shared CNN is built to capture potential interaction information and share linguistic features among all tasks. Finally, LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic. Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task, while average Acc, Pre, and Rec are increased by 0.5%, 1.4%, and 0.4%, respectively. More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.

A Study of English Translation under Newmark's Theory of Text Categories: The Case of Lin Yutang's Translation of the Peach Blossoms

As a masterpiece of ancient Chinese prose, The Peach Blossoms enjoys a high reputation in China. In recent years, with China’s economic and social development as well as its international status, more and more attention has been paid to cultural propaganda. Based on Newmark’s text-categories translation theory, this article analyzes the version translated by Lin Yutang and outlines the difficulties when translating this expressive text of literary works.

Рецензия на книгу: Гричин С. В. Авторизационная модель научного текста: монография. – Новосибирск: Изд-во НГТУ, 2020. – 292 с.

This article is a review of the book The Evidential Model of a Scientific Text by Sergey Grichin. The monograph describes evidentiality, one of the leading discursive categories of a scientific text. Grichin answers the questions: How does new knowledge emerge? How is it converted into a text? How does the dialogue between the author and his predecessors come about? Answering these questions, Grichin substantiates the status of evidentiality as a universal text-forming category. This category reflects all cognitive actions of the author of the text, so the study of evidentiality requires a complex, interdisciplinary approach. In the reviewed book, this approach is represented by the synthesis of functional stylistics, cognitive linguistics, discourse analysis, psychology and logic. The author of the monograph fully and clearly formulates the research methodology and defines the key concepts: evidential sense, evidential unit, evidential indicators, etc. Grichin establishes the extra-linguistic factors influencing the representation of evidential meanings in a scientific text, and describes the linguistic units for expressing these meanings. The correlation between the category of evidentiality and the category of certainty / uncertainty is shown. Regularities in the perception of evidential information by the addressee are defined. The novelty of the research lies in the fact that the category of evidentiality is presented as an integrative, multifactor model, which takes into account extralinguistic (discursive) and linguistic factors of communicative and cognitive activity of the author of the text. The scientific significance of the developed model is ensured by the fact that Grichin interprets evidentiality in a wider and deeper way than it was accepted in traditional linguistics (traditionally evidentiality is understood as referring to the source of someone else’s speech). The monograph considers evidentiality as a multifactorial category, which is a tool for structuring scientific discourse. The evidentiality model is developed by Grichin on the basis of the analysis of vast material, which includes more than 300 texts in 13 branches of science. To check the validity of the concept, journalistic and colloquial (dialectal) texts are used in addition to scientific texts. The regularities of the addressee’s perception of evidential senses are analysed by means of experimental methods. The reviewers note the value of the monograph for speech studies, in particular: development of a cognitive and discursive method of scientific text analysis; identification of new extralinguistic factors of scientific communication; establishment of mechanisms of generation and perception of scientific texts.

Research on part-of-speech relationship between English-Chinese translation based on corpus

Based on the self-built English and Chinese translation corpus, this paper compares the differences between Chinese translation and English original texts, including English translation and Chinese original, makes clear the scope of common parts of speech to distinguish the category of text, analyzes and thinks about the differentiation of text language classes and the correlation degree of various language characteristics, and analyzes and studies the verb changes in English and Chinese translation.

Indian News Headlines Classification using Word Embedding Techniques and LSTM Model

Newspapers introduce us to the latest happenings around the world. Going paperless creates more opportunities for newspapers, like broadcasting news coverage and presenting breaking news conveniently. News headlines are considered under the short text category and are vibrant subjects for researchers. Creating a dense vector from short texts has become a challenging and essential task in many applications such as recommender systems, context analysis, decision making, text classification, etc. This work not only targeted creating a classification model for the short text but also categorized the headlines with the ‘unknown’ category. Our work uses Bidirectional Encoder Representations from Transformers (BERT), cosine similarity index, word embedding, and Long Short-Term Memory (LSTM) network to classify news headlines in multiple categories. Our proposed method outperforms labeling the unlabeled data with the help of a BERT sentence encoder. The system uses LSTM to learn the headlines as input vectors and classify the headline text by the classifier. At the end of this experiment, the designed pipeline achieves remarkable precision at the class level.

Implementation of Machine Learning for Text Classification Using the Naive Bayes Algorithm in Academic Information Systems at Sebelas Maret University Indonesia

This study implements machine learning using the Naive Bayes algorithm to create a text classification in an engineering professional program information system. The methods used include text data collection, preprocessing, feature extraction, Naive Bayes model training, and evaluation using data testing. This study made a classification model to predict text categories with a test accuracy rate of 0.975 and a training accuracy of 0.967. This research contributes to the development of text classification in information systems and can be used as a basis for further study.

A Quantitative Analysis of Linguistic Complexities: A Case Study of English Textbooks of Federal Board of Intermediate and Secondary Education Islamabad, Pakistan

This study aims at exploring linguistic complexity of the English Text Books prescribed by the Federal Board of Intermediate and Secondary Education Islamabad, Pakistan, for intermediate classes. For the last two decades, the concept of linguistic complexity has appealed interest of the linguists and other researchers to investigate this phenomenon. There are several factors that influence linguistic complexity to a tex. The importance of a text in the process of teaching and learning is beyond any doubt, and this significance enhances in the context of the third world countries where teaching and learning is considered almost impossible in absence of textbooks. In this environment selection of a textbook without gauging its linguistic complexity and grammatical intricacy is vulnerable. Two textbooks, Text- A, for first year class, and Text- B, for second year class, were chosen as samples purposefully. To build the edifice of this research Halliday and Ure’s lexical density and grammatical intricacy methods were used. The findings reveal that the actual text of Text- A falls in the slab of the more linguistic complexity with an index of 5.1 and Text- B which falls in the category of simple texts with an average index of 4.6, as Halliday and Ure’s methods suggest. This paper concludes with recommendations that text book writers and the textbook designers must familiar with th phenomenon of linguistic complexity and grammatical density and before prescribing any text to the ultimate learners, the textbook must be examined and evaluated to measure linguistic complexity

Linguistic Category of Textuality in Narrative

The article deals with the category of textuality manifested in narrative texts. This category can be described as a system of 13 elements (subcategories). These subcategories are focused on reflecting the connections of the author and his text (the
 hierarchy of textual constructions, narrative implication, the presence of logical connections in the text, subjectivity of the presentation of the material), the author and the narrated reality (the presence of a special mode of narration, temporal localiza-
 tion, detail / conciseness of the presentation), the author and those cognitive structures that are in his mind (the ability of text fragments to be built on certain models), the author and the reader (the presence in the text of a riddle that needs to be solved by the reader, the reflection in the text of the elements of the game, into which the characters of the narrative come). The article also provides examples demonstrating
 the implementation of these text categories. The present work expresses the author’s opinion on the differentiation of linguistic, psychological and literary approaches to the narrative. Information is given about the category of textuality in its form, which has developed in modern science.

TextDC: Exploring Multidimensional Text Detection via a New Benchmark and Solution

Text detection has been significantly boosted by the development of deep neural networks but most existing methods focus on a single kind of text instance (i.e., overlaid text, layered text, scene text). In this paper, we expand the text detection task from a single dimension to multiple dimensions, thus providing multi-type text descriptions for the scene and content analysis of videos. Specifically, we establish a new task to detect and classify text instances simultaneously, termed TextDC. As far as we know, existing benchmarks cannot meet the requirements of the proposed task. To this end, we collect a large-scale text detection and classification dataset, named Text3C, which is annotated using multilingual labels, location information, and text categories. Together with the collected dataset, we introduce a multi-stage and strict evaluation metric, which penalizes detection approaches for missing text instances, false positive detection, inaccurate location boxes, and error text categories, developing a new benchmark for the proposed TextDC task. In addition, we extend several state-of-the-art detectors by modifying the prediction head to solve the new task. Then, a generalized text detection and classification framework is designed and formulated. Extensive experiments using the updated methods are conducted on the established benchmark to verify the solvability of the proposed task, the challenges of the dataset, and the effectiveness of the solution.

Feature selection based on ACO and knowledge graph for Arabic text classification

ABSTRACT Nowadays, multi-class learning processes have attracted a vast amount of attention due to their complexities and progressive development in various fields. This study focuses on the prediction of semantic categories of Arabic hadith texts by combining the Knowledge-Graph (KG) with the Ant Colony Optimisation (ACO) method. Knowledge-Graph is constructed based on the correlation between features and categories. The links’ weights depend on how frequently the features and the categories occur together, either directly or indirectly. Knowledge-Graph also directly gauges the weights of the links among features. Finally, the ants traverse the graph to select features the most prominent. Features are important whenever they are mentioned frequently in Hadith texts, and they belong to one or fewer categories. It is worth considering that the more relationships between one feature and multiple categories, the less likely it is to be selected. In our analytical experiments, six famous books of the Prophetic Sunnah were used, including more than thirty thousand hadiths with a number of categories slightly under 120. On the experimental side, we found promising results when the ACO-KG model has combined with machine learning classifiers, namely an increase of 3% in the classification task.

Revisiting the English Textbook Accessibility based on the Universal Design Learning Principles

Textbooks based on the teaching and learning process should be relevant to any students including students with disability, there are a lot of textbooks that are not suitable enough to provide accessible material. The goal of this study was to evaluate Universal Design for Learning (UDL) concepts in 2 online modules on UDL that were taught over one semester alongside English as a Foreign Language (EFL) lectures. The module is divided into 10 chapters that each discuss one of five categories of exploratory texts. To clarify the ULD concepts used in the curriculum, this research applied Textbook analysis using the Center for Applied Special Technology (CAST 2018) design framework. The results demonstrate that more ULD concepts, which notably provide multiple means of engagement, representation, and action & expression, can be used to provide better accommodation for blind students to support inclusion in educational materials. In addition, a strong desire to use the UDL concepts in their future teaching is encouraged through university modules. There is a discussion of restrictions and repercussions.

DIALOGUE AND THE CATEGORY OF DIALOGISM IN THE UKRAINIAN MASS MEDIA DISCOURSE

The article is devoted to the study of the illogical nature of Ukrainian mass media discourse, namely newspaper text and dialogue as a form of textual communication. The study substantiates the concept of dialogue as a way of organizing speech and dialogism as a text category. Dialogue represents communicative interaction of two communicators and requires the participants of communication to comply with certain conditions. The dialogism of the text is broader than the concept of dialogue, because this category is expressed regardless of the nature of speech, is a representation of the inner essence of the text as a process and result of communicative interaction.
 This theory is aimed at the study of dialogism as a defining feature of text and discourse in a broad sense. As for the newspaper text, two types of dialogism are distinguished: external and internal. The first concerns intertextual interaction and coincides with the concept of intertextuality. Internal dialogism is subject-text related to the factors of the addressee and addressee of the newspaper text. The article describes the means of representing the textual category of dialogism in heading complexes containing interrogative-corresponding dialogical units; it considers the main models in which the category of dialogism is implemented are defined, namely: dialogical units, quasi-dialogical constructions and two-part headings with an open question; dialogical interaction between communicators of media discourse in the interview genre, in which the category of dialogism is most fully realized. Regarding the text of the interview, two types of communicative interaction are distinguished: addressee-dominant and addresser-dominant type. The former type is determined by the priority role of the interlocutor. The latter is characterized by the fact that the speaker takes an active position and determines the vector and content of the communicative act, that is, the author's comments and actualization of the author's point of view prevail in the text.

A MODEL OF THE ENGLISH-UKRAINIAN SUB-CORPUS OF TEXTS OF NATO, UN AND WTO OFFICIAL AND BUSINESS DOCUMENTS

A MODEL OF THE ENGLISH-UKRAINIAN SUB-CORPUS OF TEXTS OF NATO, UN AND WTO OFFICIAL AND BUSINESS DOCUMENTS

ANALYSIS OF CUSTOMER EXPERIENCE IN THE HOTEL BUSINESS

In the process of marketing research, it is necessary to process large volumes of accumulated reviews, so it is obvious that the process of processing and analysis should be automated. Modern technical capabilities allow you to create an information and analytical system for formalized analysis of consumer reviews about hotels. The analysis of consumer reviews contributes to the development of the hotel business, since reviews are a qualitative way to assess the effectiveness of the company. With their help, it becomes clear what elements of the hotel complex should be paid attention to with the aim of their possible improvement in the future. The trends observed in reviews also allow you to find out what consumers expect and how best of all you can live up to their expectations. In fact, the task of analyzing the tonality of the text is equivalent to the problem of text classification, where tonal estimates can be categories of texts. To teach the classification model, a set of marked data downloaded from the Internet was used. The set contains 18,500 reviews about the hotels of Europe in Russian with the tonality indicated for them. The article is devoted to the urgent problem of determining the consumer attitude to services provided in the hotel business based on reviews from the Internet. A solution to the problem of analyzing the tonality of the entities of the hotel, extracted from the reviews about the hotel. To solve the problem, the MLP algorithm is selected, since the task of analyzing tonality is reduced to the task of classification, where tonal estimates are classification classes. Particular attention is paid to the algorithm for teaching a classification model developed using the Keras library in Python. To solve the task of analyzing the tonality of entit

Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot

Technological developments affect many aspects, one of which is historical education. History lessons can shape students' personalities and encourage an interest in historical knowledge. There are many stories from Indonesian history, one of which is the Sumedang Larang Kingdom. The Sumedang Larang Kingdom is one of the Islamic kingdoms in Pasundan. However, not many people know about this kingdom. The millennial generation is technologically advanced, so they can take advantage of technological advances to quickly introduce the history of Sumedang Larang. One of them utilizes the telegram bot using the Application Programming Interface (API), which can connect the system to the telegram platform. In addition, this technology can be used as a history learning attraction using the question answering system (QA). Our research aims to build a QA system that can introduce the history of Sumedang Larang to the millennial generation. Because this system uses ontology knowledge with concepts related to the Sumedang Larang domain, it can focus on the history of Sumedang Larang. Applying the support vector machine (SVM) algorithm to process classification text can make it easier to search for text categories. The test results show the performance of the SVM method with a test size parameter of 0.5, such as 74% and 78%. The performance test results are accuracy scores in the subject category and object classification.

Wizerunek Japończyków w polskich tygodnikach: analiza tekstów z Narodowego Korpusu Języka Polskiego

This article investigates texts from Polish weeklies found in the National Corpus of Polish that contain the word Japończyk ‘a Japanese’ in all possible grammatical cases. The analysis shows that it is possible to distinguish nine thematic categories of texts on the basis the following relevant facets: traits of character; physical features; associated motifs; exoticism; image of a leader on the international arena; artistic sensitivity; diplomatic relations; historical and war context; and social issues. Some texts portray the Japanese people positively, others negatively (for example, positively in texts related to politeness, negatively in those related to social issues). However, most of the texts describe the Japanese people in a neutral way. Additionally, the analysis shows that the term “pro-Japanese” (projapoński), used to describe Poland in the Japanese press, may be incorrectly interpreted by the public.

A Text Classification Model via Multi-Level Semantic Features

Text classification is a major task of NLP (Natural Language Processing) and has been the focus of attention for years. News classification as a branch of text classification is characterized by complex structure, large amounts of information and long text length, which in turn leads to a decrease in the accuracy of classification. To improve the classification accuracy of Chinese news texts, we present a text classification model based on multi-level semantic features. First, we add the category correlation coefficient to TF-IDF (Term Frequency-Inverse Document Frequency) and the frequency concentration coefficient to CHI (Chi-Square), and extract the keyword semantic features with the improved algorithm. Then, we extract local semantic features with TextCNN with symmetric-channel and global semantic information from a BiLSTM with attention. Finally, we fuse the three semantic features for the prediction of text categories. The results of experiments on THUCNews, LTNews and MCNews show that our presented method is highly accurate, with 98.01%, 90.95% and 94.24% accuracy, respectively. With model parameters two magnitudes smaller than Bert, the improvements relative to the baseline Bert+FC are 1.27%, 1.2%, and 2.81%, respectively.

The Need to Say More: A Qualitative Analysis of Added Free Text to Chronic Pain Patient Intake Packages

BackgroundMany outpatient chronic pain clinics administer extensive patient intake questionnaires to understand patients’ pain and how it impacts their lives. At our institution's pain clinic, many patients include free text in these predominantly closed-ended questionnaires, but little is known about the content categories included in this free text. AimThis study examined free text entries on chronic pain patient intake questionnaires. MethodWe analyzed 270 occurrences of free text across 43 patient intake questionnaires of people living with chronic pain using a qualitative content analytic approach. ResultsWe identified two overarching thematic categories of free text: (1) what they say (characterizes the topic of the free text); and (2) why they say it (characterizes perceived limitations of the patient intake questionnaire format). We also documented the frequency highlighting how often themes and their associated sub-themes (detailed below) were indicated. Within the What they say category, three main themes emerged: (1) health (34.9%; e.g., pain); (2) health service use (27.9%; e.g., medication); and (3) psychosocial factors (20.9%; e.g., relationships). Within the Why they say it category, four main themes emerged: (1) adding information (86.0%; e.g., elaborates/contextualizes); (2) narrow response options (65.1%; e.g., varies); (3) problems with the question (18.6%; e.g., not applicable); and (4) response error (4.7%; e.g., answers incorrectly/misinterprets question). ConclusionsPeople living with chronic pain appear motivated to add additional, unprompted information to their patient intake questionnaires. The results from this study may inform changes to chronic pain patient intake questionnaires which could facilitate improvements in chronic pain patient-health care provider communication.

Text Categories in the Title of a Short Story (on the collection of stories “How We Are Hungry” by Dave Eggers).

The article discusses the role of the title in the manifestation of text categories in a short story. The material for the study is the collection of short stories "How We Are Hungry" by a contemporary American writer Dave Eggers which is of interest in many respects, in particular in terms of formal, content and functional peculiarities of short story titles. The analysis has shown that the titles of this collection are rather diverse both in form and functions they perform; they correlate differently with the text of the work, and set different vectors for predicting the content and modality of the text. All the titles of the collection actualize the categories of integrity, cohesion and coherence. In the vast majority of the titles the categories of informativity, subjectivity and intentionality are realized, the last two being leading and most significant. The analysis allows to conclude that the manifestation of text categories in the titles of D. Eggers’ short stories is carried out in a complicated irregular way, which provokes variability of interpretations.