Natural Text Research Articles

Generating Ontology-Learning Training-Data through Verbalization

Ontologies play an important role in the organization and representation of knowledge. However, in most cases, ontologies do not fully cover domain knowledge, resulting in a gap. This gap, often expressed as a lack of concepts, relations, or axioms, is usually filled by domain experts in a manual and tedious process. Utilizing large language models (LLMs) can ease this process; a fine-tuned LLM could receive as input up-to-date and reliable domain knowledge natural text and output a structured graph in OWL RDF/Turtle format, which is the standard format of ontologies. Thus, to fine-tune a model, text-owl sentence pairs that constitute such a dataset must be acquired. Unfortunately, such a dataset does not exist in the literature or within the open-source community. Therefore, this paper introduces our LLM-assisted verbalizer to create such a data set by converting OWL statements from existing ontologies into natural text. We evaluate the verbalizer on 322 classes from four different ontologies using two different LLMs, achieving precision and recall as high as 0.99 and 0.96, respectively.

A Method for Measuring Word Sequence Complexity of Text

ABSTRACT In linguistics and natural language processing, measuring text features is crucial for representing and revealing the properties of texts in terms of topics, genres, sentiment and more. Current methods predominantly rely on the frequency of linguistic units and rarely consider the syntagmatic properties of these units, which can reflect deeper linguistic characteristics (e.g. syntax and pragmatics). This paper proposes a general method for calculating the sequence complexity of text. By combining the features of word length and word frequency, two specific implementations of this method are provided. Using these two formulas, word sequence complexity of text based on the Brown Corpus and the Gutenberg Corpus is calculated. The results show that word sequence complexity of text indicates a characteristic of gradual stabilization. By comparing random texts with natural texts, it is found that word sequence complexity is influenced by syntactic rules rather than sentence order, and natural texts tend to alternate between words of varying lengths and frequencies. Classification experiment results indicate that the proposed word sequence complexity outperforms commonly used quantitative indicators for representing the genre of text, such as TTR, information entropy, Zipf’s law parameters and motifs.

Soft set-based MSER end-to-end system for occluded scene text detection, recognition and prediction

The presence of unpredictable occlusions on natural scene text is a significant challenge, exacerbating the difficulties already posed on text detection and recognition by the variability of such images. Addressing the need for a robust, consistently performing approach that can effectively address the above challenges, this paper presents a new Soft Set-based end-to-end system for text detection, recognition and prediction in occluded natural scene images. This is the first approach to integrate text detection, recognition and prediction, unlike existing systems developed for end-to-end text spotting (text detection and recognition) only. For candidate text components detection, the proposed combination of Soft Sets with Maximally Stable Extremal Regions (SS-MSER) improves text detection and spotting in natural scene images, irrespectively of the presence of arbitrarily orientated and shaped text, complex backgrounds and occlusion. Furthermore, a Graph Recurrent Neural Network is proposed for grouping candidate text components into text lines and for fitting accurate bounding boxes to each word. Finally, a Convolutional Recurrent Neural Network (CRNN) is proposed for the recognition of text and for predicting missing characters due to occlusion. Experimental results on a new occluded scene text dataset (OSTD) and on the most relevant benchmark natural scene text datasets demonstrate that the proposed system outperforms the state-of-the-art in text detection, recognition and prediction. The code and dataset are available at https://github.com/alloydas/Softset-MSER-Based-Occluded-Scene-Text-Spotting/blob/master/Soft_set_MSER.ipynb

Taking relations as known conditions: A tagging based method for relational triple extraction

Relational triple extraction refers to extracting entities and relations from natural texts, which is a crucial task in the construction of knowledge graph. Recently, tagging based methods have received increasing attention because of their simple and effective structural form. Among them, the two-step extraction method is easy to cause the problem of category imbalance. To address this issue, we propose a novel two-step extraction method, which first extracts subjects, generates a fixed-size embedding for each relation, and then regards these relations as known conditions to extract the objects directly with the identified subjects. In order to eliminate the influence of irrelevant relations when predicting objects, we use a relation-special attention mechanism and a gate unit to select appropriate relations. In addition, most current models do not account for two-way interaction between tasks, so we design a feature interactive network to achieve bidirectional interaction between subject and object extraction tasks and enhance their connection. Experimental results on NYT, WebNLG, NYT⋆ and WebNLG⋆ datasets show that our model is competitive among joint extraction models.

Features and constituting of musical reality

In the article the author addresses the topic of music temporality. A hypothesis is introduced that on the one hand, music is a form of life as a solution to personal problems, while on the other hand, it is an artistic reality. The main characteristics of artistic reality, i.e. textual nature, the unity of experiencing its events and constructing them, and the role of the individual, are discussed. The concepts of “expressive means”, “material of art”, and “art form” are analyzed, and it is argued that the whole in music is determined by the expressive means and personality of the artist. In the last part of the article, the author substantiates the initial hypothesis about the nature of music, compares music with other temporary forms of art, and shows that classical music was shaped as a form of life, allowing the new European personality to realize three main plans of its existence.

Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.

Distinct neural pathway and its information flow for blind individual's Braille reading

Natural Braille reading presents significant challenges to the brain networks of late blind individuals, yet its underlying neural mechanisms remain largely unexplored. Using natural Braille texts in behavioral assessments and functional MRI, we sought to pinpoint the neural pathway and information flow crucial for Braille reading performance in late blind individuals. In the resting state, we discovered a unique neural connection between the higher-order ‘visual’ cortex, the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in late blind individuals, but not in sighted controls. The left-lateralized LOC-IFC connectivity was correlated with individual Braille reading proficiency. Prolonged Braille reading practice led to increased strength of this connectivity. During a natural Braille reading task, bidirectional information flow between the LOC and the IFC was positively modulated, with a predominantly stronger top-down modulation from the IFC to the LOC. This stronger top-down modulation contributed to higher Braille reading proficiency. We thus proposed a two-predictor multiple regression model to predict individual Braille reading proficiency, incorporating both static connectivity and dynamic top-down communication between the LOC-IFC link. This work highlights the dual contributions of the occipito-frontal neural pathway and top-down cognitive strategy to superior natural Braille reading performance, offering guidance for training late blind individuals.

MODEL IMPLEMENTATION AND COMPREHENSIVE STUDY ON VISUAL AIR TYPING SYSTEMS

This paper explores the implementation and applications of visual air typing systems, focusing on fingertip detection and text recognition to provide an alternative to the traditional input methods. Although models with similar functionality exist, our study emphasizes how such systems can offer practical solutions for users with visual impairments and enhance user experience in virtual reality (VR) environments. By detecting fingertip movements and translating them into written words, such models present a seamless and intuitive user experience. For individuals with visual impairments, traditional keyboards can be challenging, and sign language lacks uniformity across different regions. An air typing system addresses these issues by offering a consistent text input method. Additionally, in VR applications where traditional keyboards are impractical, it enables more natural and efficient text entry. This paper provides a comprehensive review of existing air typing technologies, detailing their strengths and limitations. We discuss the potential future directions for air typing systems, highlighting the need for more inclusive and versatile input methods. Our findings indicate that air typing can significantly enhance user interaction in diverse environments, making it a valuable alternative to traditional keyboards. Overall, this study underscores the practical applications and benefits of air typing systems, particularly for visually impaired users and VR contexts, contributing to advancing more inclusive technological solutions.

Use of digital technologies in making and processing texts of a new nature in modern education: A theoretical review

Use of digital technologies in making and processing texts of a new nature in modern education: A theoretical review

A vision-language foundation model for the generation of realistic chest X-ray images.

The paucity of high-quality medical imaging datasets could be mitigated by machine learning models that generate compositionally diverse images that faithfully represent medical concepts and pathologies. However, large vision-language models are trained on natural images, and the diversity distribution of the generated images substantially differs from that of medical images. Moreover, medical language involves specific and semantically rich vocabulary. Here we describe a domain-adaptation strategy for large vision-language models that overcomes distributional shifts. Specifically, by leveraging publicly available datasets of chest X-ray images and the corresponding radiology reports, we adapted a latent diffusion model pre-trained on pairs of natural images and text descriptors to generate diverse and visually plausible synthetic chest X-ray images (as confirmed by board-certified radiologists) whose appearance can be controlled with free-form medical text prompts. The domain-adaptation strategy for the text-conditioned synthesis of medical images can be used to augment training datasets and is a viable alternative to the sharing of real medical images for model training and fine-tuning.

English Prepositions Expressing Temporal Limits: Semantic Features and Functioning

This article explores the semantics and usage of prepositions in the English that denote temporal limits of actions. A review of various lexicographical sources, reference literature, and grammars is conducted, highlighting that not all sources provide comprehensive information on the meanings and functioning of prepositions, often offering only general information. Practical material for the study is extracted from the British National Corpus, comprising approximately two thousand contexts. It is established that making a definitive conclusion about including a temporal limit within the timeframe allocated for an action is not always possible. This semantic ambiguity is noted in around 20% of the total number of analyzed contexts. The study demonstrates that to resolve ambiguity and ensure successful communication, addressees often resort to using clarifying elements or complete / partial paraphrasing of the message text. Clarifications are found to be most typical in texts of a scientific research nature, schedules, legal and economic documents. The article concludes on the necessity of further research related to the semantics and usage of prepositions.

Functions of idioms in English as lingua franca: An appraisal system account

AbstractIdiomatic expressions are not just neutral equivalents of their literal counterparts, but exert some sort of evaluative force. This study employed the appraisal system (AS) to explore the functions of idiomatic expressions and their variations in English as a lingua franca context, and to compare different academic domains in terms of use of evaluative language in the form of idiomatic expressions. To this end, the corpus of English as a lingua franca in academic settings and the Vienna–Oxford International Corpus of English were searched for idiom tokens through whole‐phrase and keyword search. The idioms and their variations were classified based on the AS. The findings indicated that social and behavioral scientific texts included higher proportions of evaluative idiomatic expressions whereas medical and natural scientific texts generated lower percentages of appraisal idioms. There was a stronger tendency in both corpora toward using idioms as appreciation as compared to judgment and affect. Given the employed methodology, the AS could not be applied to some idioms for their compositionality nature.

Linguistic steganalysis via multi-task with crossing generative-natural domain

In the real world, the sources of text often transcend the boundaries of generative and natural domains, which introduces challenges for existing linguistic steganalysis methods. The typical problem arises from the sample selection bias caused by training solely on a single-source domain, rendering the model incapable of inferencing across the entire generative-natural (GN) space. Moreover, there exists the problem of overlooking the sensitive discrepancies between generative text and natural text, which hampers model fitting. In this paper, we model steganalysis in a brand-new perspective by employing multi-task learning to build the main task and auxiliary tasks in the cross GN domain. The proposed Cross Generative-Natural Domain Multi-task Model (CG-NDMM) can concurrently address the two aforementioned issues through i) modeling steganalysis across the entire GN space, incorporating two auxiliary tasks alongside a main task, and ii) utilizing a feature representation transfer learning strategy to harmonize two sub-networks. Furthermore, we employ diverse steganography algorithms to construct the datasets, which comprise four types of texts (generative-cover, generative-steganographic, natural-cover, and natural-steganographic) derived from two public datasets, Movie and Twitter. The experiments on these datasets demonstrate the effectiveness of the proposed approach, showcasing its substantially superior performance over the comparative baseline methods.

A corpus-based study of live grammatical metaphor in English academic writing

ABSTRACT Nominalization is an important resource for creating grammatical metaphor (GM). Previous research shows that nominalization is characteristic of academic writing but does not demonstrate discipline sensitivity within the academic genre. The reason might be that some nominalizations have developed into dead GM. This study aims to identify nominalizations that create live GM and explore the discipline sensitivity of live GMs based on a corpus of academic article abstracts across three discipline groups. The findings indicate that a clause construes an event or a status, which is either conveyed by the verbal group or a nominal group functioning as a participant of the verbal group. Verb nominalizations construing events and adjective nominalizations construing statuses are the key factors in identifying live GM. Notably, nominalizations not cooccurring with verbalizations are not necessarily dead GMs. The corpus-based research shows that both verb and adjective nominalizations exhibit discipline sensitivity. Verb nominalizations are more prevalent in natural science texts, whereas adjective nominalizations are more prevalent in the humanities and social science texts. However, live GMs are not inherently discipline sensitive; rather, it is those GMs contributing to the lexical density of the text that are discipline sensitive.

Written vs generated text: “naturalness” as a textual and psycholinguistic category

In the context of the development of text generation technologies, the opposition “naturalness − unnaturalness of text” has been transformed into a new dichotomy: “naturalness – artificiality”. The aim of this article is to investigate the phenomenon of naturalness in this context from two perspectives: analyzing the linguistic characteristics of a natural text against a generated (artificial) text and systematizing introspective perceptions of Russian native speaker informants as to what a “natural” text should be like and how it should differ from a generated text. The material for the study was a parallel corpus of film reviews in Russian, consisting of two subcorpora: reviews written by people and those generated by a large language model based on prompts, which are the beginnings of reviews, from the first subcorpus. The following methods were applied for the comparative analysis of the two subcorpora: computer-assisted text processing for calculating the values of 130 metrics of text linguistic complexity, psycholinguistic experiment, expert text analysis, contrastive analysis. As a result, it was determined that from the point of view of their own linguistic characteristics, “natural” texts differ from generated texts mainly by greater flexibility of syntactic structure, allowing both omission or reduction of structures and redundancy, as well as by slightly greater lexical variability. Naturalness as a psycholinguistic category is related to the informants’ autostereotypical ideas about the cognitive characteristics of people as a species. The analysis of texts erroneously attributed by informants (generated, labelled as natural and vice versa) showed that a number of characteristics of this autostereotype are overestimated by informants, while others, in general, correlate with the linguistic specificity of texts from the subcorpus of written reviews. In conclusion, we formulate definitions of naturalness as a textual and psycholinguistic category.

Understanding and Framing Change in Islamic Law: Potentials and Possible Pitfalls of the Concepts of Canonization and Codification

Abstract Conservative and populist notions of Islamic law, or Sharīʿa, emphasize its immutability. While the textual nature of traditional Islamic jurisprudence supports this conception, academic research in recent decades demonstrates that Islamic law saw constant change and development. We also witness an increasing acknowledgement that several processes of change do not always follow the rich and advanced emic narratives and theories of how change allegedly did or should happen. The possibility of change is also intimately connected to theories of authority, where normative and descriptive models may give different pictures. Several historians and anthropologists of Islamic law have resorted to ‘canonization’ and ‘codification’ as concepts employed to understand and frame significant change, partly intending to create an analytical distance to the study object, unrestricted by the Islamic tradition. While the two concepts have hitherto been employed separately, they have one thing in common: their focus on selection and authorization. This article will first demonstrate how these concepts have been used in Islamic legal studies and then further argue that by juxtaposing and comparing these two concepts, new insights can be gained regarding their potentials and pitfalls.

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion.

Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds. To address the challenge of detecting diverse texts in natural scenes, this paper proposes a multi-scale natural scene text detection method based on attention feature extraction and cascaded feature fusion. This method combines global and local attention through an improved attention feature fusion module (DSAF) to capture text features of different scales, enhancing the network's perception of text regions and improving its feature extraction capabilities. Simultaneously, an improved cascaded feature fusion module (PFFM) is used to fully integrate the extracted feature maps, expanding the receptive field of features and enriching the expressive ability of the feature maps. Finally, to address the cascaded feature maps, a lightweight subspace attention module (SAM) is introduced to partition the concatenated feature maps into several sub-space feature maps, facilitating spatial information interaction among features of different scales. In this paper, comparative experiments are conducted on the ICDAR2015, Total-Text, and MSRA-TD500 datasets, and comparisons are made with some existing scene text detection methods. The results show that the proposed method achieves good performance in terms of accuracy, recall, and F-score, thus verifying its effectiveness and practicality.

On current limitations of online eye-tracking to study the visual processing of source code

Context:Eye-tracking is an increasingly popular instrument to study how programmers process and comprehend source code. While most studies are conducted in controlled environments with lab-grade hardware, it would be desirable to simplify and scale participation in experiments for users sitting remotely, leveraging home equipment. Objective:This study investigates the possibility of performing eye-tracking studies remotely using open-source algorithms and consumer-grade webcams. It establishes the technology’s current limitations and evaluates the quality of the data collected by it. We conclude by recommending ways forward to address the shortcomings and make remote code-reading studies in support of eye-tracking feasible in the future. Method:We gathered eye-gaze data remotely from 40 participants performing a code reading experiment on a purpose-built web application. The utilized eye-tracker worked client-side and used ridge regression to generate x- and y-coordinates in real-time predicting the participants’ on-screen gaze points without the need to collect and save video footage. We processed and analysed the collected data according to common practices for isolating eye-movement events and deriving metrics used in software engineering eye-tracking studies. In response to the lack of an algorithm explicitly developed for detecting oculomotor fixation events in low-frequency webcam data, we also introduced a dispersion threshold algorithm for that purpose. The quality of the collected data was subsequently assessed to determine the adequacy and validity of the methodology for eye-tracking. Results:The collected data was found to be of varying quality despite extensive calibration and graphical user guidance. We present our results highlighting both the negative and positive observations from which the community hopefully can learn. Both accuracy and precision were low and ultimately deemed insufficient for drawing valid conclusions in a high-precision empirical study. We nonetheless contribute to identifying critical limitations to be addressed in future research. Apart from the overall challenge of vastly diverse equipment, setup, and configuration, we found two main problems with the current webcam eye-tracking technology. The first was the absence of a validated algorithm to isolate fixations in low-frequency data, compromising the assurance of the accuracy of the data derived from it. The second problem was the lack of algorithmic support for head movements when predicting gaze location. Unsupervised participants do not always keep their heads still, even if instructed to do so. Consequently, we frequently observed spatial shifts that corrupted many collected datasets. Three encouraging observations resulted from the study. Even when shifted, gaze points were consistently dispersed in patterns resembling both the shape and size of the stimuli without extreme deviations. We could also distinguish recognizable reading patterns. Linearity was significantly different when participants were reading source code compared to natural text, and we could detect the expected left-to-right and top-to-bottom reading directions for participants reading natural text snippets. Conclusion:The accuracy and precision levels were not sufficient for a word-by-word analysis of code reading but could be adequate for a broader, coarse-grained precision study. Additionally we identified two main issues compromising the collected data validity and contributed a fixation detection algorithm to approach one of these issues. With suitable solutions to the identified issues, remote eye-tracking studies with webcams on code reading could eventually be feasible.

METHODS OF TEACHING ENGLISH USING NATIONAL RESOURCES IN EXTRACURRICULAR ACTIVITIES

The article considers the issue of improving the methodology of developing students' speech competencies in extracurricular English classes, developing listening comprehension, reading and writing skills through work on texts of a national nature.

Identifying Main Themes in Diabetes Management Interviews Using Natural Language Processing-Based Text Mining.

This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program experience and satisfaction. Semistructured interview questions were used, and the interviews were auto-recorded. The interview transcripts were preprocessed and analyzed using four natural language processing-based text-mining techniques. The top 30 words from the term frequency and term frequency-inverse document frequency each were derived. In the N-gram analysis, the connection strength of "diabetes" and "education" was the highest, and the simultaneous connectivity of word chains ranged from a maximum of seven words to a minimum of two words. Based on the CONvergence of iteration CORrelation (CONCOR) analysis, three clusters were generated, and each cluster was named as follows: participation in a diabetes education program to control blood glucose, exercise, and use of digital devices. This study using text mining proposes a new and useful approach to visualize data to develop patient-centered diabetes education.