Piece Of Text Research Articles

Upgrading the Newsroom

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level word embeddings to help both modeling morphologically rich languages, e.g., German, and transferring knowledge across nearby languages. The text encoder adopts a hierarchical self-attention mechanism to attend more to both key words within a piece of text and informative components of a news article. We extensively experiment our system on a large-scale text-image database containing multimodal multilingual news articles collected from Swiss local news media websites. The system is compared with multiple baselines with ablation studies and is shown to beat existing text-image retrieval methods in a weakly supervised learning setting. Besides, we also offer insights on the advantage of using multiple textual sources and multilingual data.

On the status of wh-exclamatives in English

Abstract Exclamative expressions like What an enormous crowd came! and How wonderful this journey is! have been described as forming one of the four basic sentence (or clause) types of English. The present paper discusses the main features of this type and analyzes them with reference to the framework of Discourse Grammar. It is argued that the structure of exclamatives can be related to other sentence types in terms of discourse strategies that are used for constructing texts, in particular by means of cooptation, a cognitive-communicative strategy that enables speakers to use existing text pieces for new discourse functions.

Relative contributions of Shakespeare and Fletcher in Henry VIII: An analysis based on most frequent words and most frequent rhythmic patterns

Abstract The versified play Henry VIII is nowadays widely recognized to be a collaborative work not written solely by William Shakespeare. We employ combined analysis of vocabulary and versification together with machine learning techniques to determine which other authors took part in the writing of the play and what were their relative contributions. Unlike most previous studies, we go beyond the attribution of particular scenes and use the rolling attribution approach to determine the probabilities of authorship of pieces of texts, without respecting the scene boundaries. Our results highly support the canonical division of the play between William Shakespeare and John Fletcher proposed by James Spedding, but also bring new evidence supporting the modifications proposed later by Thomas Merriam.

Automatically learning construction injury precursors from text

In light of the increasing availability of digitally recorded safety reports in the construction industry, it is important to develop methods to exploit these data to improve our understanding of safety incidents and ability to learn from them. In this study, we compare several approaches to automatically learn injury precursors from raw construction accident reports. More precisely, we experiment with two state-of-the-art deep learning architectures for Natural Language Processing (NLP), Convolutional Neural Networks (CNN) and Hierarchical Attention Networks (HAN), and with the established Term Frequency - Inverse Document Frequency representation (TF-IDF)+Support Vector Machine (SVM) approach. For each model, we provide a method to identify (after training) the textual patterns that are, on average, the most predictive of each safety outcome. We show that among those pieces of text, valid injury precursors can be found. The proposed methods can also be used by the user to visualize and understand the models' predictions.

Deriving the sentiment polarity of term senses using dual-step context-aware in-gloss matching

Vital to the task of Sentiment Analysis (SA), or automatically mining sentiment expression from text, is a sentiment lexicon. This fundamental lexical resource comprises the smallest sentiment-carrying units of text, words, annotated for their sentiment properties, and aids in SA tasks on larger pieces of text. Unfortunately, digital dictionaries do not readily include information on the sentiment properties of their entries, and manually compiling sentiment lexicons is tedious in terms of annotator time and effort. This has resulted in the emergence of a large number of research works concentrated on automated sentiment lexicon generation. The dictionary-based approach involves leveraging digital dictionaries, while the corpus-based approach involves exploiting co-occurrence statistics embedded in text corpora. Although the former approach has been exhaustively investigated, the majority of works focus on terms. The few state-of-the-art models concentrated on the finer-grained term sense level remain to exhibit several prominent limitations, e.g., the proposed semantic relations algorithm retrieves only senses that are at a close proximity to the seed senses in the semantic network, thus prohibiting the retrieval of remote sentiment-carrying senses beyond the reach of the ‘radius’ defined by number of iterations of semantic relations expansion. The proposed model aims to overcome the issues inherent in dictionary-based sense-level sentiment lexicon generation models using: (1) null seed sets, and a morphological approach inspired by the Marking Theory in Linguistics to populate them automatically; (2) a dual-step context-aware gloss expansion algorithm that ‘mines’ human defined gloss information from a digital dictionary, ensuring senses overlooked by the semantic relations expansion algorithm are identified; and (3) a fully-unsupervised sentiment categorization algorithm on the basis of the Network Theory. The results demonstrate that context-aware in-gloss matching successfully retrieves senses beyond the reach of the semantic relations expansion algorithm used by prominent, well-known models. Evaluation of the proposed model to accurately assign senses with polarity demonstrates that it is on par with state-of-the-art models against the same gold standard benchmarks. The model has theoretical implications in future work to effectively exploit the readily-available human-defined gloss information in a digital dictionary, in the task of assigning polarity to term senses. Extrinsic evaluation in a real-world sentiment classification task on multiple publically-available varying-domain datasets demonstrates its practical implication and application in sentiment analysis, as well as in other related fields such as information science, opinion retrieval and computational linguistics.

Literary translation: implantation vs transference

The figurative language employed by authors, which reflects their styles of writing, is one main reason behind the challenges that most literary translators encounter when dealing with literary works. Usually employed for aesthetic and poetic purposes, figures of speech imply connotative meanings. In literary works, words are used only assigns to settle down the flying spirits of meanings and ideas so that the audience can have a thread that could lead them to intended meanings. I believe that literary translators should face the challenges of translating literary works through two main approaches. First, transferring the work of art as it is without trying to find any equivalent in the target language for any piece of text in the source language. The aim of such type of translation would be familiarizing the audience in the target language with the literature and culture of the source language. Second, translating the SL work of art creatively, i.e. using all possible strategies and procedures to find natural equivalents in the TL for any stylistic features in the SLT. This type of translation should aim at pleasing and entertaining the TL audience.

Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations

A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate and adapt Cross-View Training (CVT) as a semi-supervised learning technique, allowing us to utilize unlabeled data to improve the model representations. In the Thai sentence segmentation experiments, our model reduced the relative error by 7.4% and 10.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. We also applied our model to the task of pronunciation recovery on the IWSLT English dataset. Our model outperformed the prior sequence tagging models, achieving a relative error reduction of 2.5%. Ablation studies revealed that utilizing n-gram presentations was the main contributing factor for Thai, while the semi-supervised training helped the most for English.

Speech and Opinion Recognition from a Conversation

Speech Recognition is an interdisciplinary technique used to convert spoken language into text. It is a sub domain of computational linguistics and can be implemented using Machine Learning and Deep Learning Algorithms. Opinion Mining or Sentiment Analysis is a process which enables identifying opinions expressed by an author in a piece of text computationally. This opinion refers to the polarity of the expressed opinion, i.e. positive or negative. Through this research work, we aim to combine these two natural language processing techniques and devise a system that can take speech as the input and determine the sentiment behind the speakers’ words. The subject of the speech input may vary but the end goal is to recognize whether the attitude of the speaker towards the subject was positive or negative. The input will be converted to text and this text will then be classified using several different machine learning techniques. These include Naïve Bayes’ Classifier, Support Vector Machine, Logistic Regression and Decision Trees. After classification, the results for the three classifiers will be predicted and compared. Future scope of the project includes creating an ensemble of these classifiers to get better accuracy and precision of determining the sentiment of the speaker.

Unifying Parsing and Reflective Printing for Fully Disambiguated Grammars

Language designers usually need to implement parsers and printers. Despite being two closely related programs, in practice they are often designed separately, and then need to be revised and kept consistent as the language evolves. It will be more convenient if the parser and printer can be unified and developed in a single program, with their consistency guaranteed automatically. Furthermore, in certain scenarios (like showing compiler optimisation results to the programmer), it is desirable to have a more powerful reflective printer that, when an abstract syntax tree corresponding to a piece of program text is modified, can propagate the modification to the program text while preserving layouts, comments, and syntactic sugar. To address these needs, we propose a domain-specific language BiYacc, whose programs denote both a parser and a reflective printer for a fully disambiguated context-free grammar. BiYacc is based on the theory of bidirectional transformations, which helps to guarantee by construction that the generated pairs of parsers and reflective printers are consistent. Handling grammatical ambiguity is particularly challenging: we propose an approach based on generalised parsing and disambiguation filters, which produce all the parse results and (try to) select the only correct one in the parsing direction; the filters are carefully bidirectionalised so that they also work in the printing direction and do not break the consistency between the parsers and reflective printers. We show that BiYacc is capable of facilitating many tasks such as Pombrio and Krishnamurthi’s ‘resugaring’, simple refactoring, and language evolution.

Quality and Readability Assessment of Websites on Human Papillomavirus and Oropharyngeal Cancer.

The incidence of human papillomavirus-positive (HPV+) oropharyngeal cancer is rising, but public knowledge about this diagnosis remains low. This study aimed to investigate the quality and readability of online information about HPV+ oropharyngeal cancer. Cross-sectional website analysis. This study conducted a total of 12 web searches across Google, Yahoo, and Bing to identify websites related to HPV+ oropharyngeal cancer. The QUality Evaluation Scoring Tool (QUEST) was used to measure quality based on seven website criteria. The Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL) were used to measure readability, with scores estimating the education level a reader would require to understand a piece of text. Readability improves as FRES increases and FKGL decreases. Twenty-seven unique web pages were evaluated. The mean USA reading grade level as measured by FKGL was 10.42 (standard deviation = 1.54). There was an inverse relationship between quality and readability, with a significant positive correlation between QUEST score and FKGL (r = 0.343, P = .040) and a significant negative correlation between QUEST score and FRES (r = -0.537, P = .002). With a mean USA reading grade level more than four grades above the American Medical Association's recommendation and results indicating that readability suffers as quality improves, these findings suggest that the currently available online information about HPV+ oropharyngeal cancer is insufficient. Improved patient education practices and resources about this diagnosis are needed. NA Laryngoscope, 131:87-94, 2021.

QASC: A Dataset for Question Answering via Sentence Composition

Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

Extractive Text Summarization for Sports Articles using Statistical Method

The past decade has endorsed a great rise in Artificial Intelligence. Text summarization which comes under AI has been an important research area that identifies the relevant sentences from a piece of text. By Text Summarization, we can get short and precise information by preserving the contents of the text. This paper presents an approach for generating a short and precise extractive summary for the given document of text. A statistical method for extractive text summarization of sports articles using extraction of various features is discussed in this paper. The features taken are TF-ISF, Sentence Length, Sentence Position, Sentence to Sentence cohesion, Proper noun, Pronoun. Each sentence is given a score known as the predictive score is calculated and the summary for the given document of text is given based on the predictive score or also known as the rank of the sentence. The accuracy is checked using the BBC Sports Article dataset and sports articles of various newspapers like the New York Times, CNN. The precision of 73% is acquired when compared with System Generated Summary (SGS) and manual summary, on an average.

Deep Learning: Credit Default Prediction from User-Generated Text

Digital technologies produce vast amounts of unstructured data that can be stored and accessed by traditional banks and fintechs. Prior literature on the topic indicates that certain aspects of this unstructured data could be valuable for decisions regarding the acceptance and pricing of credit contracts. Both, practitioners and academics are interested in whether this information is, in fact, valuable and how to exploit it for credit risk predictions. We employ deep learning techniques to extract credit-relevant information from user-generated text on Lending Club. Our results confirm that even short pieces of user-generated text can improve credit default predictions significantly. We benchmark four deep neural network architectures and more traditional approaches to retrieving credit-relevant information from text. An average embedding network outperforms all other deep learning architectures. The deep learning models outperform the more traditional approaches in almost all cases. Among the traditional approaches, spelling mistakes are particularly informative.

Stance Detection

Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: { Favor , Against , Neither }.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.

DEVELOPMENT OF INFORMATION VISUALIZATION METHODS FOR USE IN MULTIMEDIA APPLICATIONS

The aim of the article is development of a technique for visualizing information for use in multimedia applications. In this study, to visualize information, it is proposed first to compile a list of key terms of the subject area and create data tables. Based on the structuring of fragments of the subject area, a visual display of key terms in the form of pictograms, a visual display of key terms in the form of images, and a visual display of data tables are performed. The types of visual structures that should be used to visualize information for further use in multimedia applications are considered. The analysis of existing visual structures in desktop publishing systems and word processors is performed. To build a mechanism for visualizing information about the task as a presentation, a multimedia application is developed using Microsoft Visual Studio software, the C# programming language by using the Windows Forms application programming interface. An algorithm is proposed for separating pieces of information text that have key terms. Tabular data was visualized using the “parametric ruler” metaphorical visualization method, based on the metaphor of a slide rule. The use of the parametric ruler method on the example of data visualization for the font design of children's publications is proposed. Interaction of using the method is ensured due to the fact that the user will enter the size of the size that interests for it and will see the ratio of the values of other parameters. The practical result of the work is the creation of a multimedia application “Visualization of Publishing Standards” for the visualization of information for the font design of publications for children. The result of the software implementation is the finished multimedia applications, which, according to the standardization visualization technique in terms of prepress preparation of publications, is the final product of the third stage of the presentation of the visual form

Tag recommendation by text classification with attention-based capsule network

Tag recommendation has been attracting much attention with the growth of digital resources. The goal of a tag recommendation system is to provide a set of tags for a piece of text to ease the tagging process done manually by a user. These tags have been shown to enhance the capabilities of search engines for navigating, organizing and searching content. However, tag text manually is time-consuming and labor-intensive. In this paper, we introduce a tag recommendation by text classification. We explore the capsule network with dynamic routing for the tag recommendation task. The capsule network encodes the intrinsic spatial relationship between a part and a whole constituting viewpoint invariant knowledge that automatically generalizes to novel viewpoints. In addition, an attention mechanism is incorporated into the capsule network to distill important information from the input documents. We conduct extensive experiments on large publication datasets to evaluate the effectiveness of our model. The experimental results demonstrate that our model substantially outperforms the compared baseline methods and achieves the state-of-the-art results.

Sentiment analysis using rule-based and case-based reasoning

Sentiment analysis becomes increasingly popular with the rapid growth of various reviews, survey responses, tweets or posts available from social media like Facebook or Twitter. Sentiment analysis can be turned into the question of whether a piece of text is expressing positive, negative or neutral sentiment towards the discussed topic and can be thus understood as a knowledge-based classification problem. A variety of knowledge-based techniques can be used to solve this problem. The paper focuses on two complementary approaches that originate in the area of AI (artificial intelligence), rule-based reasoning and case-based reasoning. We describe basic principles of both approaches, their strengths and limitations and, based on a review of literature, show how these approaches can be used for sentiment analysis.

The growth, spread, and mutation of internet phenomena: A study of memes

An internet memes is defined as “an image, video, piece of text, etc., typically humorous in nature, that is copied and spread rapidly by Internet users, often with slight variations” (Oxford Living Dictionary, 2018). Such units of information are spread rapidly by internet users, often for a relatively short period of time. The modeling of this spread is of interest for a variety of reasons, especially since large amounts of information are spread via this method. In this study, we attempt to evaluate some previously-implemented models of memetic diffusion and improve upon them when possible. In particular, we implement an SIR-based system of stochastic differential equations.

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter.

Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

ОСОБЛИВОСТІ ЕТИМОЛОГІЇ ТА ЛЕКСИЧНОЇ СТРУКТУРИ АНГЛІЙСЬКИХ НАЗВ СТРАВ

The article considers such aspects of modern English vocabulary as the names of culinary dishes, food consumption and the history of their names. In modern conditions of intercultural communication the formation, strengthening of intercultural relations, specific features of each language come to the fore. Food itself is one of the main manifestations of cultural relationships today. That is why the study of various aspects of gastronomic discourse, which are influenced by different national cuisines in our rapidly changing modern world, is gaining new impetus. Under the gastronomic discourse in our work we understand a special kind of verbal and social discourse, the purpose of which is to achieve a certain type of communication. It is a piece of text or language related to the eating process in which the participants are considered, the conditions, the ways of communication, the environment in which the conversation takes place. In modern conditions of intercultural communication, the formation, strengthening of intercultural relations, specific features of each gastronomic preference have been formed over the centuries, and, of course, features such as geographical location, climate, religion, traditions and foundations, as well as economic factors could not affect them. . It is often enough to just look at what a person eats to determine where he comes from, what beliefs he has and what lifestyle he leads. It is known that the linguistic personality exists in a certain culture and has basic values – cultural concepts. The concepts that exist in the collective consciousness are essential for both the individual and for collective cultural identity in general. The problem of defining the concept of "food" as a cultural concept in modern language theory in the 21st century is of great interest to linguists and linguistic and cultural scientists. This concept in linguistic culture is one of the least studied and defined, although its meanings and cultural values ​​are quite high. The food and cuisine of any nation are integral to the language and are reflected in its vocabulary. In the system of national values, the cultural concept of "food" occupies one of the key places. This phenomenon can be viewed from different angles.