Accurate Text Research Articles

СОПОСТАВИТЕЛЬНЫЙ АНАЛИЗ РОССИЙСКИХ И АНГЛОЯЗЫЧНЫХ РЕКЛАМНЫХ ТЕКСТОВ

The purpose of the study is to reveal the essence of the advertising text in English-speaking countries and in Russia. The article focuses not only on the differences, but also on the similarities in the construction of slogans. The influence of parts of speech on motivation when buying a certain product or service is determined. The scientific novelty consists in the consideration of English-language and Russian-language advertising texts on the same basis, the identification of distortion of advertising slogans in the translation process. As a result of the study, the distinctive features of English-language and Russian-language media texts were identified. It is emphasized that, depending on the language in which the media text is presented, the method of presenting the material is modified. Based on the analysis, it was revealed that the English-speaking audience is more liberated, as a result, the advertising message is presented in a free and open form, often with elements of humor. The Russian-speaking audience prefers a more accurate advertising text, minimizing the use of slang.

Detection and recognition of text traffic signs above the road

Based on the similarity between traffic sign images in the source and target domains, the parameters migrated from the source domain are utilised as the initial parameters of the faster region convolutional neural network (Faster-R-CNN), which is trained for detecting text traffic signs, and then fine-tune the network parameters based on the samples in target domain for obtaining the final network parameters. Moreover, the converted traffic sign images from RGB to HSV space is also used as the training samples of the network, thereby overcoming the under-learning problem of model caused by less training samples. The traditional efficient and accurate scene text (EAST) detection network model is tailored and a new recognition model is proposed based on the extreme learning machine (ELM) classifier to identify and classify the detected text traffic signs above the road. Experimental results in the natural scene demonstrate the effectiveness of the proposed method.

Design and Implementation of English Text Recognition System under Robot Vision

The traditional methods of English letter visual recognition are machine learning and contour feature extraction. However, limited by the hardware resources such as camera and processor, it is difficult for the robot to realize fast and accurate text extraction and recognition. By comparing the results of domain average method, median filtering method and KNNF filtering method in text image preprocessing of robot, this paper proposes to use median filtering method for image preprocessing and corner detection method for text area extraction, which improves the efficiency of text image processing for robot. Based on the writing characteristics of English letters, a concentric rectangle tangent based on equal difference area is proposed The overall structure of the algorithm solves the problem of fast recognition of multi-directional mixed text, effectively reduces the influence of the angle between robot vision and pre recognition target on text recognition, and improves the speed and accuracy of text recognition by optimizing SOFM network. The system model has been verified based on Nao robot platform.

Exploring teacher interactive e-feedback on students’ writing through Google Docs: factors promoting interactivity and potential for learning

ABSTRACT Teacher feedback has been reported to be challenging for learners to understand and use productively in revising their writing, especially when it is provided in a monologic manner. There have thus been calls for teachers to ensure feedback is more ‘dialogic’ or ‘interactive’, encouraging students to become active respondents to feedback rather than mere receivers of it. However, teacher dialogic/interactive feedback in face-to-face settings may be time-consuming and demanding for teachers. This motivated us to think of how electronic (e-)feedback practices could be made more interactive through the use of technological tools. This study, based on the instructor’s written feedback on 10 undergraduates’ L2 academic writing for a course in language and linguistics in a Malaysian public university, identifies several factors clustered under three dimensions: teacher-related, task-related and learner-related factors that promote teacher–learner interactions in teacher e-feedback in Google Docs. Interactive feedback also contributed to students’ discussion of issues in their writing, engagement in accurate text revisions and negotiation of feedback. The study discusses pedagogical implications for instructors in promoting interactive feedback practices in writing courses.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results. Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability and implementationWe make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology. Supplementary data are available at Bioinformatics online.

A deceptive review detection framework: Combination of coarse and fine-grained features

Electronic commerce has become a popular shopping mode. To enhance their reputations, attract more customers, and finally obtain more benefits, dishonest sellers often recruit buyers or robots to post a large number of deceptive reviews to mislead users. According to the interpretability of learning results, existing methods for detecting deceptive reviews can be mainly divided into explicit feature-based mining ones and neural network-based implicit feature mining ones. The nature of these works is accurate text classification based on coarse-grained features (e.g., topic, sentence, and document) or fine-grained features (e.g., word). To take full merits of existing approaches, this paper proposes a new framework that explores a method to combine the coarse-grained features and the fine-grained features. In this framework, the coarse-grained implicit semantic features of the topic distribution are learned by the concatenation of a Latent Dirichlet Allocation (LDA) topic model and a 2-layered neural network. The fine-grained implicit semantic features from the word vectors representation of the reviews are parallelly learned by a deep learning framework. Finally, these two granular features are combined and adopted to train a Support Vector Machine (SVM) classifier for detecting whether a review is deceptive or not. To verify the effectiveness and performance of this framework, we derive three models by specifying three popular deep learning models, such as TextCNN, long short-term memory (LSTM), and Bi-directional LSTM (BiLSTM) to learn the fine-grained features. Experimental results on a mixed-domain dataset and balanced/unbalanced in-domain datasets show that all the combination models are superior to the corresponding baseline models considering single features.

Developing a new approach to summarize Arabic text automatically using syntactic and semantic analysis

Due to the wide spread information and the diversity of its sources, there is a need to produce an accurate text summary with the least time and effort. This summary must preserve key information content and overall meaning of the original text. Text summarization is one of the most important applications of Natural Language Processing (NLP). The goal of automatic text summarization is to create summaries that are similar to human-created ones. However, in many cases, the readability of created summaries is not satisfactory,   because the summaries do not consider the meaning of the words and do not cover all the semantically relevant aspects of data. In this paper we use syntactic and semantic analysis to propose an automatic system of Arabic texts summarization. This system is capable of understanding the meaning of information and retrieves only the relevant part. The effectiveness and evaluation of the proposed work are demonstrated under EASC corpus using Rouge measure. The generated summaries will be compared against those done by human and precedent researches. Â

A survey study on Arabic WordNet: baring opportunities and future research directions

WordNet (WN) plays an essential role in knowledge management and information retrieval - as it allows for a better understanding of word relationships, which leads to more accurate text processing. The success of WN for the English language encouraged researchers to develop WNs for other languages. One of the most common of such languages is Arabic. However, the current state of affairs of Arabic WN (AWN) has not been properly studied. Thus, this paper presents a survey study on AWN conducted to explore opportunities and possible future research directions. The results involve the synthesis of over 100 research papers on AWN. These research papers were divided into categories and subcategories.

3e Symphonie en ut mineur by Camille Saint-Saëns, and: Poèmes symphoniques by Camille Saint-Saëns

Reviewed by: 3e Symphonie en ut mineur by Camille Saint-Saëns, and: Poèmes symphoniques by Camille Saint-Saëns James Brooks Kuykendall Camille Saint-Saëns. 3e Symphonie en ut mineur, op. 78. Edited by Michael Stegemann. Kassel: Bärenreiter, 2016. (Œuvres instrumentales complètes, Sér. I, Vol. 3) [Front matter in Fr., Eng., and Ger.: foreword, p. vii–xii; preface, p. xiii–lxxxiv; documents, p. lxxxvi–cv; score p. 3–192; crit. rep. in Fr. p. 195–213. Cloth. ISMN 979-0-006-55950-3. €365 ($438).] Camille Saint-Saëns. Poèmes symphoniques [Le Rouet d'Omphale, op. 31; Phaéton, op. 39; Danse macabre, op. 40; La Jeunesse d'Hercule, op. 50]. Edited by Hugh Macdonald. Kassel: Bärenreiter, 2019. (Œuvres instrumentales complètes, Sér. I, Vol. 4) [Front matter in Fr., Eng., and Ger.: foreword, p. vii–xii; preface., p. xiii–lviii; documents, p. lx–lxxiv; score p. 3–241; crit. rep. in Eng. p. 245–60. Cloth. ISMN 979-0-006-56115-5. €378 ($454).] Camille Saint-Saëns was involved in the first wave of monumental editions, serving as general editor of Œuvres complètes of Jean-Phillipe Rameau (published by Durand in eighteen volumes between 1894 and 1924, but never completed). Now, it seems, it is his turn. An astonishingly prolific composer, Saint-Saëns started early and stayed active to the end of his very long life (1835–1921). The sheer quantity of material poses a significant challenge to the publication of a complete edition of his works. The two volumes reviewed here are from a series aspiring to comprise only his instrumental works, but even so this entails a projected thirty-six volumes. It is an ambitious and even worthy endeavor, but it is off to a shaky start. Michael Stegemann points out in his series foreword that most of Saint-Saëns's instrumental works "have not been newly edited since their first publication (and are often full of errors, typical for this period)" (p. ix of both volumes). That may be true of the corpus as a whole, but it is not the case of the works under review here—each of which appeared in a second edition during the composer's lifetime, and the texts of these corrected second editions were actually very good—judging from a review of the original sources and these volume editors' decisions. Indeed, as Hugh Macdonald notes, for three of the symphonic poems—all but Le Rouet d'Omphale—"all the sources agree closely with one another" (pp. 252, 259). And there are a numerous sources to collate in this repertoire: autographs of the orchestral version as well as keyboard versions, two editions of the full score and the performing materials, sometimes sketches and marked proofs, plus derivative transcriptions. Even in the case of Le Rouet d'Omphale, the 1906 second edition of the full score presents an accurate text, correcting a number of errors in the first. The differences between the second edition and this new edition almost [End Page 319] entirely involve the regularization of articulations and dynamics. (The only error I spotted in the new edition of this work [m. 48, Vc and Cb] is obvious enough that any user can recognize it as such, and so does little harm.) The Poèmes symphoniques volume is fine, but it hardly makes a strong case for the need for a new edition of any of these works: we may not have had as complete a grasp of the textual situation until Macdonald had finished the project, but the texts presented here have negligible differences from what was already in the public domain. Is an œuvres complètes of his instrumental music necessary? I shall return to this issue below. The first volume of the new edition to be published was Symphony no. 3, one of the composer's best loved and most impressive works, and perhaps intended to serve as the flagship for the edition as a whole. The work is in a similar textual situation to the symphonic poems: Durand's 1907 second edition is actually very accurate. The problem—and it is a...

Adolf Behne e a construção do Moderno – Parte 1

O livro A Construção Funcional Moderna, escrito por Adolf Behne em 1923, contém discussões fundamentais para a compreensão (e condução) da Arquitetura Moderna na década de 20. Em seu texto, o autor identifica correntes e categorias de pensamento diversas, e as explora em três tempos, determinando os três capítulos fundamentais de seu livro. O primeiro deles, objeto desse texto e tradução, tem como ênfase a recusa na adoção de princípios formais estabelecidos, e operação de retorno à finalidade para gerar um edifício vivo. O efeito desta operação reposiciona o conceito de edifício, libertando-o de uma condição estática e fachadista. Behne apoia-se nas casas de Frank Lloyd Wright dos anos 1900 para desenvolver seu argumento, e termina por escrever um texto acurado e preciso sobre a obra de Wright, apontando características que só se acentuariam na obra do arquiteto norte-americano nas décadas de 1930 e 40.

Streaming video database features: What do faculty and students really want?

The adoption of streaming video by academic libraries is widespread, yet little is known about the experiences of faculty and students who use streaming video. This article describes a usability study of three popular streaming video databases. Faculty and student participants completed a series of tasks in each database and answered questions about their use of streaming video. Faculty liked complete and accurate metadata and text descriptions, the use of thumbnail images, accessibility, ease of use, the availability of film segments, the ability to easily share content, and the availability of time-stamped, hyperlinked transcripts. Students liked easy-to-use, intuitive interfaces.

A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining

The amount of biomedical literature is vast and growing quickly, and accurate text mining techniques could help researchers to efficiently extract useful information from the literature. However, existing named entity recognition models used by text mining tools such as tmTool and ezTag are not effective enough, and cannot accurately discover new entities. Also, the traditional text mining tools do not consider overlapping entities, which are frequently observed in multi-type named entity recognition results. We propose a neural biomedical named entity recognition and multi-type normalization tool called BERN. The BERN uses high-performance BioBERT named entity recognition models which recognize known entities and discover new entities. Also, probability-based decision rules are developed to identify the types of overlapping entities. Furthermore, various named entity normalization models are integrated into BERN for assigning a distinct identifier to each recognized entity. The BERN provides a Web service for tagging entities in PubMed articles or raw text. Researchers can use the BERN Web service for their text mining tasks, such as new named entity discovery, information retrieval, question answering, and relation extraction. The application programming interfaces and demonstrations of BERN are publicly available at https://bern.korea.ac.kr .

Morphological Verb-Aware Tibetan Language Model

The Tibetan language model (TLM) is the key to Tibetan natural language processing. In this paper, we first observe that, different from widely used languages, Tibetan contains many morphological verbs that rarely appear in natural sentences but play a key role in accurate text prediction. This property is usually ignored by existing methods and makes traditional training strategies less effective in constructing accurate and robust TLMs. Hence, we propose a morphological verb-aware TLM by offline learning via a character frequency reweighting strategy and online tuning of discriminative weights conditioned on morphological verbs. However, because of the influence of morphological verbs on the tense and semantics of sentences, it is necessary to consider the morphological verbs in Tibetan. As a result, compared with state-of-the-art methods, our method not only reduces the perplexity but also improves the character error on tasks of the text prediction and automatic speech recognition (ASR).

Deep Web Data Source Classification Based on Text Feature Extension and Extraction

With the growth of volume of high quality information in the Deep Web, as the key to utilize this information, Deep Web data source classification becomes one topic with great research value. In this paper, we propose a Deep Web data source classification method based on text feature extension and extraction. Firstly, because the data source contains less text, some data sources even contain less than 10 words. In order to classify the data source based on the text content, the original text must be extended. In text feature extension stage, we use the N-gram model to select extension words. Secondly, we proposed a feature extraction and classification method based on Attention-based Bi-LSTM. By combining LSTM and Attention mechanism, we can obtain contextual semantic representation and focus on words that are closer to the theme of the text, so that more accurate text vector representation can be obtained. In order to evaluate the performance of our classification model, some experiments are executed on the UIUC TEL-8 dataset. The experimental result shows that Deep Web data source classification method based on text feature extension and extraction has certain promotion in performance than some existing methods.

Network text sentiment analysis method combining LDA text representation and GRU-CNN

In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.

DETECTION OF SUSPICIOUS TERRORIST EMAILS USING TEXT CLASSIFICATION: A REVIEW

This paper provides a comprehensive review and analysis of the detection of suspicious terrorist electronic mails (emails) using various phases and methods of text classification. We explored, analyzed, and compared different datasets, features, feature extraction techniques, feature representation techniques, feature selection schemes, text classification techniques, and performance measurement metrics used in the detection of suspicious terrorist e-mails. 30 articles were retrieved from 6 well-known academic databases after rigorous selection. From the study, we found that researchers often generate their own e-mails dataset since there is no public dataset is available in the research area of detecting suspicious terrorist e-mails. In most of the studies, researchers used content and context-based features to detect terrorist e-mails. Our findings also show that the most commonly used feature extraction techniques are the bag of words and n-gram, the most typically applied feature representation schemes are binary representation and term frequency, the most usually adopted feature selection method is information gain,, the most common and most accurate text classification algorithms are naive bayes, decision trees, and support vector machines, and the widely employed performance measurement metrics are accuracy, precision, and recall. Open research challenges and research issues that involve significant research efforts are also summarized in this review for future researchers in the area of suspicious terrorist e-mail detection using text classification techniques where the critical analysis presented in this paper also provides valuable insights to guide these researchers. Finally, the indicated issues and challenges presented in this paper can be used as future research directions in this area.

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

The main goal of this work is to provide the cognitive computing community with valuable resources to analyze and simulate the intentionality and/or emotions embedded in the language employed in social media. Specifically, it is focused on the Spanish language and online dialogues, leading to the creation of Sofoco (Spanish Online Forums Corpus). It is the first Spanish corpus consisting of dialogic debates extracted from social media and it is annotated by means of crowdsourcing in order to carry out automatic analysis of subjective language forms, like sarcasm or nastiness. Furthermore, the annotators were also asked about the context need when taking a decision. In this way, the users’ intentions and their behavior inside social networks can be better understood and more accurate text analysis is possible. An analysis of the annotation results is carried out and the reliability of the annotations is also explored. Additionally, sarcasm and nastiness detection results (around 0.76 F-Measure in both cases) are also reported. The obtained results show the presented corpus as a valuable resource that might be used in very diverse future work.

The Effects of Direct and Indirect Corrective Feedback on Accuracy in Second Language Writing

The effectiveness of providing Corrective Feedback (CF) on L2 writing has long been a matter of considerable debate. A growing body of research has been conducted to investigate the value of various types of CF on improving grammatical accuracy in the writing of English as a second or foreign language. This article is mainly concerned with the role of Corrective Feedback (CF) in developing the L2 writers’ ability to produce an accurate text, and argues that CF is considered to be one of the fundamental techniques in teaching second language (L2) writing. Bearing this in mind, it attempts to maintain the effectiveness of CF on the L2 students’ abilities to develop the accuracy of their written output. This topic has recently produced a significant interest among both teachers and researchers in the areas of L2 writing and second language acquisition. A key issue to be addressed is the degree to which CF effectively helps the second language writers obtain long-term accuracy. Currently, the author of this paper has been conducting a PhD study on the effect of direct and indirect corrective feedback on the academic writing accuracy of Kurdish EFL university students, and the data was collected from writing testing samples (pre-test, post-test and delayed post-test) produced by105 undergraduate students of English department from two public universities. The results could be obtained from the study should have important implications for L2 writing practitioners and researchers.

The Risks In Translating Smartphone Strings For Medical Study Smartphones Apps & Smartwatches

Recently there has been an increase in the ‘Bring Your Own Device’ approach to clinical studies as a means of participants carrying out Patient Reported Outcomes (PROs) on their own smartphones or smartwatches. The aim of this study is to evaluate the risks in translating smartphone strings used in such apps and to highlight solutions in conveying the context of individual strings. We will focus on a study related to mobility being translated into France-French. This Linguistic Validation project underwent the following steps outlined by the ISPOR Principles of Good Practice: Preparation (including concept elaboration); Dual Forward Translation; Reconciliation; Editing; Dual Back Translation; Back Translation Review and Proofreading. During Back Translation Review, the risks in translating smartphone strings were analysed, establishing which translations needed further clarification and updates. When reviewing the Back Translations, 7% of items required updates. Of these; 82% were misinterpretations of the source due to lack of context and 18% were formatting updates to ensure the translations were short enough to fit within a smartphone screen. Without being able to refer to the English source in screenshot form, translating these smartphone strings into French presented two specific areas of risks. 1) Many translations were too long due to additional clarifications being required in French. 2) French occasionally required several translations depending on the specific context of each source string (e.g. ‘skip’ was both ‘ignoré’ and ‘non prise’). To provide clear and accurate text for the app, it is important to provide linguists with screenshots and concept elaborations from project initiation. This aids the linguist to retain the appropriate character length for the smart-device displays. French texts often require elaboration so developers should consider flexibility and precision of language when developing the app. Both elements ensure accurate Linguistic Validation of smartphone strings and a reduced need for edits.

Keys for the morphological identification of the Australian paralysis ticks (Acari: Ixodidae), with scanning electron micrographs.

The Australian paralysis ticks (Ixodes: Sternalixodes) are of considerable medical and veterinary importance within Australia. This is because of their ability to cause paralysis but also their capacity to transmit pathogens and induce allergic reactions. The available keys for the identification of the group (Ixodes holocyclus Neumann, Ixodes cornuatus Roberts, and Ixodes hirsti Hassall) are ambiguous and contain errors, making accurate identification of the group difficult. Illustrations of varying quality and accuracy of these species have been published in the past; however, they have never been collectively treated using scanning electron microscopy (SEM). Female Australian paralysis ticks are here examined using SEM, and the first pictorial key for this group is presented along with an accurate text key.