Word Space Research Articles

Script Struggles: A Psycholinguistic Study of Handwriting Difficulties among EFL Learners

This study seeks to examine the salient manifestations of handwriting difficulties experienced by learners of English as a Foreign Language (EFL), throwing light on the cognitive, neural, and educational aspects of handwriting. Qualitative and quantitative research designs were adopted to provide a comprehensive understanding of the manifestations of handwriting difficulties among EFL learners. In total, 80 college-level EFL students aged between18 to 22 years were included in this study. To investigate the specific manifestations of handwriting difficulties faced by the EFL learner population, data were gathered using a handwriting task given to all participants. The results of this investigation showed that the participants performed less than optimally in key handwriting features, including letter size consistency, legibility, word spacing, letter spacing, letter shape, left margin, and writing speed. Based on the findings, the study concluded with proposed interventions and instructional strategies to enhance the legibility and fluency of EFL learners' handwriting.

Introducing Mr. Three: Attention, Perception, and Meaning Selection in the Acquisition of Number and Color Words.

Children learn their first number words gradually over the course of many months, which is surprising given their ability to discriminate small numerosities. One potential explanation for this is that children are sensitive to the numerical features of stimuli, but don't consider exact cardinality as a primary hypothesis for novel word meanings. To test this, we trained 144 children on a number word they hadn't yet learned, and contrasted this with a condition in which they were merely required to attend to number to identify the word's referent, without encoding number as its meaning. In the first condition, children were trained to find a "giraffe with three spots." In the second condition, children were instead trained to find "Mr. Three", which also named a giraffe with three spots. In both conditions, children had to attend to number to identify the target giraffe, but, because proper nouns refer to individuals rather than their properties, the second condition did not require children to encode number as the meaning of the expression. We found that children were significantly better at identifying the giraffe when it had been labeled with the proper noun than with the number word. This finding contrasted with a second experiment involving color words, in which children (n = 56) were equally successful with a proper noun ("Mr. Purple") and an adjective ("the giraffe with purple spots"). Together, these findings suggest that, for number, but not for color, children's difficulty acquiring new words cannot be solely attributed to problems with attention or perception, but instead may be due to difficulty selecting the correct meaning from their hypothesis space for learning unknown words.

An AI-assisted terahertz reconfigurable metamaterial in standard 180-nm CMOS

For large-scale terahertz (THz) reconfigurable metamaterials (RM), each unit element needs to be controlled by an independent voltage source. This leads to a huge solution space for control words to achieve the required RM performance specification. In this paper, we propose a fast AI-assisted control-word generation scheme to reduce the computation cost of electromagnetic (EM) simulations (Forward Model) and to speed up iterations for control word searches (Inverse Model). We demonstrate the effectiveness of our models by designing and fabricating a 12 × 9 RM array with a Bi-layer Bi-Split-Ring Resonator structure (B-BSRR) in standard 180 nm CMOS technology. The terahertz RM can be electrically reconfigured, providing active metamaterials with an amplitude modulation depth of around 0–1.7 dB at 0.49 THz. The results show that our Forward Model can predict the S-parameters between 100 GHz and 800 GHz with a mean absolute error of 0.61 dB. Our method is more than 200 times faster than traditional full-wave simulation methods. The Inverse Model can generate required control words within a 1.3 dB error margin in less than 200 iterations.

Knowledge-injected prompt learning for actionable information extraction from crisis-related tweets

In crisis situations, emergency response organizations can derive significant benefits from actionable information available on social media. Such information provides effective guidance and support for rescue operations. Unlike traditional sources of information, social media reflects the latest developments during a crisis, as well as actionable suggestions and the needs of affected individuals. However, existing methods that leverage pre-trained language models for this task require a large amount of annotated data for fine-tuning. This data acquisition process is costly and impractical in some data-scarce scenarios. To address this challenge, we propose a Knowledge-injected Actionable Information Extraction Model (KAIEM) that can extract actionable information from crisis-related tweets in few-shot and zero-shot settings. Based on prompt learning, KAIEM automatically constructs the label word space of verbalizers using word embeddings, eliminating the need for manual design or external knowledge bases. This mechanism enables our model to make accurate predictions without relying on labeled data for fine-tuning. Additionally, KAIEM employs knowledge probing to inject conceptual knowledge into prompt templates, thereby enhancing the semantics of crisis-related tweets. We conducted a series of experiments on a real-world crisis-related dataset. The experimental results demonstrate the effectiveness of KAIEM in few-shot and zero-shot scenarios.

Legibility of variable message signs on foggy highway: Effect of text color and spacing

Variable message signs are frequently utilized as highway communication facilities to inform vehicles about the state of the roads and the weather. However, standards and design specifications for variable message signs are unclear. This study examines the effect of the own factors and environmental factors on the legibility of variable message signs in order to optimize the design of variable message signs on highways. Three font colors (green, red, and yellow) and three levels of word spacing (0.1H, 0.3H, and 0.5H) were chosen for the variable message signs. For the environmental factors, three different weather types were represented according to visibility, namely sunny (visibility 10,000 m), light fog (visibility = 1000), and heavy fog (visibility = 200). The legibility time and distance were recorded for 23 participants, and then the legibility of the variable message signs was analyzed. The results showed that own factors and environmental factors influenced the legibility of the variable message signs. Experiment 1 results showed that the legibility of the variable message signs was the best when the text was yellow in light fog weather conditions. Experiment 2 results showed that the legibility of the variable message signs was better than other experimental scenarios in light fog conditions with a 0.3H word spacing. The findings of this study will be used by relevant agencies to establish design guidelines for variable message signs.

The Impact of Font Design Based on Cognitive Psychology on Reading Experience

From the perspective of cognitive psychology, this paper examines and analyses how Font design affects the reading experience through four dimensions: visual perception, attention allocation, cognitive load and emotional response. It explores the effects of font attributes such as readability, glyph characteristics and word spacing on reading speed, comprehension and memory retention. It also proposes principles of font design based on cognitive psychology, aiming to provide guidance for designers and content creators to enhance users' reading efficiency and satisfaction.

Digital Phenotypes for Early Detection of Internet Gaming Disorder in Adolescent Students: Explorative Data-Driven Study.

Limited awareness, social stigma, and access to mental health professionals hinder early detection and intervention of internet gaming disorder (IGD), which has emerged as a significant concern among young individuals. Prevalence estimates vary between 0.7% and 15.6%, and its recognition in the International Classification of Diseases, 11th Revision and Diagnostic and Statistical Manual of Mental Disorders, 5th Edition underscores its impact on academic functioning, social isolation, and mental health challenges. This study aimed to uncover digital phenotypes for the early detection of IGD among adolescents in learning settings. By leveraging sensor data collected from student tablets, the overarching objective is to incorporate these digital indicators into daily school activities to establish these markers as a mental health screening tool, facilitating the early identification and intervention for IGD cases. A total of 168 voluntary participants were engaged, consisting of 85 students with IGD and 83 students without IGD. There were 53% (89/168) female and 47% (79/168) male individuals, all within the age range of 13-14 years. The individual students learned their Korean literature and mathematics lessons on their personal tablets, with sensor data being automatically collected. Multiple regression with bootstrapping and multivariate ANOVA were used, prioritizing interpretability over predictability, for cross-validation purposes. A negative correlation between IGD Scale (IGDS) scores and learning outcomes emerged (r166=-0.15; P=.047), suggesting that higher IGDS scores were associated with lower learning outcomes. Multiple regression identified 5 key indicators linked to IGD, explaining 23% of the IGDS score variance: stroke acceleration (β=.33; P<.001), time interval between keys (β=-0.26; P=.01), word spacing (β=-0.25; P<.001), deletion (β=-0.24; P<.001), and horizontal length of strokes (β=-0.21; P=.02). Multivariate ANOVA cross-validated these findings, revealing significant differences in digital phenotypes between potential IGD and non-IGD groups. The average effect size, measured by Cohen d, across the indicators was 0.40, indicating a moderate effect. Notable distinctions included faster stroke acceleration (Cohen d=0.68; P=<.001), reduced word spacing (Cohen d=.57; P=<.001), decreased deletion behavior (Cohen d=0.33; P=.04), and longer horizontal strokes (Cohen d=0.34; P=.03) in students with potential IGD compared to their counterparts without IGD. The aggregated findings show a negative correlation between IGD and learning performance, highlighting the effectiveness of digital markers in detecting IGD. This underscores the importance of digital phenotyping in advancing mental health care within educational settings. As schools adopt a 1-device-per-student framework, digital phenotyping emerges as a promising early detection method for IGD. This shift could transform clinical approaches from reactive to proactive measures.

CySecBERT : A Domain-Adapted Language Model for the Cybersecurity Domain

The field of cysec is evolving fast. Security professionals are in need of intelligence on past, current and —ideally — upcoming threats, because attacks are becoming more advanced and are increasingly targeting larger and more complex systems. Since the processing and analysis of such large amounts of information cannot be addressed manually, cysec experts rely on machine learning techniques. In the textual domain, pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) have proven to be helpful as they provide a good baseline for further fine-tuning. However, due to the domain-knowledge and the many technical terms in cysec, general language models might miss the gist of textual information. For this reason, we create a high-quality dataset 1 and present a language model 2 specifically tailored to the cysec domain that can serve as a basic building block for cybersecurity systems. The model is compared on 15 tasks: Domain-dependent extrinsic tasks for measuring the performance on specific problems, intrinsic tasks for measuring the performance of the internal representations of the model, as well as general tasks from the SuperGLUE benchmark. The results of the intrinsic tasks show that our model improves the internal representation space of domain words compared with the other models. The extrinsic, domain-dependent tasks, consisting of sequence tagging and classification, show that the model performs best in cybersecurity scenarios. In addition, we pay special attention to the choice of hyperparameters against catastrophic forgetting, as pre-trained models tend to forget the original knowledge during further training.

Qualitative Writing: Exploring Cul-de-Sacs and Alleys

Writing qualitatively is an immersive experience divergent to other forms of research writing. It is an active construction of reality characterized by integration of the socio-cultural context and in-depth analysis of data viewed through the lens of the writer’s own experience of the phenomenon. However, these characteristics prove challenging and qualitative researchers are often at a loss on how to document their findings. Most studies provide a broad sweep of qualitative writing with few explicit examples in practice which further confounds researchers. Consequently, researchers find qualitative writing exacting and intimidating and tend to present their findings objectively with either scarce or excessive reference to the research paradigm, thwarting the full transformative potential of qualitative research. Drawing on my experience as an interpretive phenomenologist, I present the writing cul-de-sacs and alleys I navigated to illustrate my becoming as a qualitative writer. Specifically, I highlight three distinct strategies to write it right, qualitatively. To maximize findings, the writing style should conceptually align with the research philosophy. Textual cohesion and a multi-dimensional view of social reality can be generated through a mixed-genre writing approach. Finally, making the “I” meaningful by incorporating the researcher’s experiences into the text affords a deepened understanding of the phenomenon. Tenaciously navigating cul-de-sacs in writing and finding solutions to exit no through roads, and walking those shadowy alleys of theoretical constraints, stylistic impositions, and tight word space endow a concrete visibility to the otherwise blurry aspects of life.

Cross-Modal Match for Language Conditioned 3D Object Grounding

Language conditioned 3D object grounding aims to find the object within the 3D scene mentioned by natural language descriptions, which mainly depends on the matching between visual and natural language. Considerable improvement in grounding performance is achieved by improving the multimodal fusion mechanism or bridging the gap between detection and matching. However, several mismatches are ignored, i.e., mismatch in local visual representation and global sentence representation, and mismatch in visual space and corresponding label word space. In this paper, we propose crossmodal match for 3D grounding from mitigating these mismatches perspective. Specifically, to match local visual features with the global description sentence, we propose BEV (Bird’s-eye-view) based global information embedding module. It projects multiple object proposal features into the BEV and the relations of different objects are accessed by the visual transformer which can model both positions and features with long-range dependencies. To circumvent the mismatch in feature spaces of different modalities, we propose crossmodal consistency learning. It performs cross-modal consistency constraints to convert the visual feature space into the label word feature space resulting in easier matching. Besides, we introduce label distillation loss and global distillation loss to drive these matches learning in a distillation way. We evaluate our method in mainstream evaluation settings on three datasets, and the results demonstrate the effectiveness of the proposed method.

New marker for chronic kidney disease progression and mortality in medical-word virtual space

A new marker reflecting the pathophysiology of chronic kidney disease (CKD) has been desired for its therapy. In this study, we developed a virtual space where data in medical words and those of actual CKD patients were unified by natural language processing and category theory. A virtual space of medical words was constructed from the CKD-related literature (n = 165,271) using Word2Vec, in which 106,612 words composed a network. The network satisfied vector calculations, and retained the meanings of medical words. The data of CKD patients of a cohort study for 3 years (n = 26,433) were transformed into the network as medical-word vectors. We let the relationship between vectors of patient data and the outcome (dialysis or death) be a marker (inner product). Then, the inner product accurately predicted the outcomes: C-statistics of 0.911 (95% CI 0.897, 0.924). Cox proportional hazards models showed that the risk of the outcomes in the high-inner-product group was 21.92 (95% CI 14.77, 32.51) times higher than that in the low-inner-product group. This study showed that CKD patients can be treated as a network of medical words that reflect the pathophysiological condition of CKD and the risks of CKD progression and mortality.

Short text classification with Soft Knowledgeable Prompt-tuning

Over the past few decades, short text classification has emerged as a critical downstream task in natural language processing (NLP). One crucial classification research issue is how to advance semantic understanding considering the short length, feature sparsity, and high ambiguity in short texts. Recently, prompt-tuning has been proposed to insert a template into the input and convert text classification tasks into equivalent cloze-style tasks. However, among most of the previous approaches, either the crafted template methods are time-consuming and labor-intensive, or automatic prompt generation methods cannot achieve satisfied performance. In this paper, we introduce a novel approach called Soft Knowledgeable Prompt-tuning for short text classification. Our method considers both the template generation and classification performance to construct prompts for label prediction. We employ five different strategies to expand the label words space for modifying soft prompts, and the integration of these strategies is used as the final verbalizer. Despite being automatic, experimental results show that our method achieved more desirable performance even than the crafted template methods, outperforming the state-of-the-art by more than 14 Accuracy points on four well-known benchmarks.

Topologies for Finite Words: Compatibility with the Cantor Topology

Infinite words are often considered as limits of finite words. As topological methods have been proved to be useful in the theory of ω-languages it seems to be providing to include finite and infinite words into one (topological) space. In most cases this results in a poor topological structure induced on the subspace of finite words. In the present paper we investigate the possibility to link topologies in the space of finite words with a topology in the space of infinite words via a natural mapping. A requirement in this linking of topologies consists in the compatibility of the topological properties (openness, closedness etc.) of images with preimages and vice versa. Here, we show that choosing for infinite words the natural topology of the CANTOR space and the δ-limit as linking mapping there are several natural topologies on the space of finite words compatible with the topology of the CANTOR space. It is interesting to observe that besides the well-known prefix topology there are at least two more whose origin is from language theory – centers and supercenters of languages. We show that several of these topologies on the space of finite words fit into a class of ℒ-topologies and exhibit their special properties w.r.t. to the compatibility with the CANTOR topology.

Phrase-Level Topic Modeling Based on Joint Embedding Space of Words, Phrases and Documents

Phrase-Level Topic Modeling Based on Joint Embedding Space of Words, Phrases and Documents

Graphological Construction of Students' Writing as a Consideration for Differentiated Learning of Indonesian Language

Teachers must attempt to learn about students' learning styles, psychological conditions, emotions, and personal conditions through non-cognitive diagnostic assessments. No one can learn purely visual, auditory, or kinesthetic since they are all prejudiced. Therefore, another non-cognitive approach is needed, specifically graphology. The study aims to determine changes in learning model methodology application that are appropriate to the students' conditions. The research method used by the researcher was a qualitative descriptive method. This study describes the results of analyzing students' handwriting in X-1 class of SMA Mutiara Islami Plus Bekasi. Several categories were analyzed, including the writing slant, writing line slant, writing size, word, and line spacing in writing, as well as the letter analysis (a, t, and j). The analysis results were organized in differentiated learning using a method of Inquiry Training Learning. This handwriting analysis can assist educators in discovering and determining students' interests and talents further.

THE WORD-FORMATION POTENTIAL OF THE WORD-FORMATION NEST WITH THE ORIGINAL WORDS "ZAN"

In this article, the word-formation potential of the word-formation nest with the original word "zan" (law) is determined based on the analysis of the word-formation nest formed from this word. The works of such scientists as N. Oralbai, O. Tokkozhaeva, A. Salkynbai, K. Kurmanaliev, B. Yessimseitov, N. Kokisheva, T. Tokhtarov, D. Kuandykov, who studied the problem of the word-formation nest, are considered. A mandatory element of the word–formation nest is the source word. The original word, it is commonly called the top, plays a very important role in the formation of the word-formation nest, affects its structure, the semantic relations of single-root words in the structure of the nest. It is part of all derived words, determines their meaning. To date, the vertices in the nests can be: nouns, adjectives, numerals, verbs, adverbs. The article analyzes one of the main concepts of legal discourse – the word "law" from the point of view of the word-formation nest. 43 derived words are determined, 24 of them are terms formed in the word-formation nest by synthetic, analytical, lexico-semantic methods of word formation. The identification of synchronous word-formation potential requires the determination of the volume, depth of word–formation nest. At the same time, it was found that the volume of analyzed word-formation nest and their structure depend primarily on the volume of the lexico-semantic space of the source words. The more extensive the system of meanings a word has, the more voluminous its word-formation nest. The dependence of the volume of word-formation nest on the frequency of use of their source words was also revealed. The word-formation potential of the original words is also revealed by the depth of the word-formation nest, which means the number of syntagmatic, paradigmatic units in this particular word-formation nest. Linguistic data are collected from the Explanatory Dictionary of the Kazakh language, the Dictionary of Legal Terms and the Textbook Customs Law of the Republic of

TYPES OF LEXICAL AND GRAMMATICAL TRANSFORMATIONS IN LITERARY TRANSLATION

In this article, the word-formation potential of the word-formation nest with the original word "zan" (law) is determined based on the analysis of the word-formation nest formed from this word. The works of such scientists as N. Oralbai, O. Tokkozhaeva, A. Salkynbai, K. Kurmanaliev, B. Yessimseitov, N. Kokisheva, T. Tokhtarov, D. Kuandykov, who studied the problem of the word-formation nest, are considered. A mandatory element of the word–formation nest is the source word. The original word, it is commonly called the top, plays a very important role in the formation of the word-formation nest, affects its structure, the semantic relations of single-root words in the structure of the nest. It is part of all derived words, determines their meaning. To date, the vertices in the nests can be: nouns, adjectives, numerals, verbs, adverbs. The article analyzes one of the main concepts of legal discourse – the word "law" from the point of view of the word-formation nest. 43 derived words are determined, 24 of them are terms formed in the word-formation nest by synthetic, analytical, lexico-semantic methods of word formation. The identification of synchronous word-formation potential requires the determination of the volume, depth of word–formation nest. At the same time, it was found that the volume of analyzed word-formation nest and their structure depend primarily on the volume of the lexico-semantic space of the source words. The more extensive the system of meanings a word has, the more voluminous its word-formation nest. The dependence of the volume of word-formation nest on the frequency of use of their source words was also revealed. The word-formation potential of the original words is also revealed by the depth of the word-formation nest, which means the number of syntagmatic, paradigmatic units in this particular word-formation nest. Linguistic data are collected from the Explanatory Dictionary of the Kazakh language, the Dictionary of Legal Terms and the Textbook Customs Law of the Republic of Kazakhstan.

Aspect category sentiment analysis based on prompt-based learning with attention mechanism

Aspect category sentiment analysis (ACSA) excels at identifying the aspect categories and corresponding sentiments involved in a sentence, regardless of whether the aspect terms are explicitly mentioned or not. However, current methods tend to overinflate the original data, resulting in the introduction of unnecessary information, and fail to capture the inter-task relationship sufficiently. This paper presents a new method termed the prompt-based joint model (PBJM) to address these complications. PBJM treats the sentiment polarity prediction as binary classification and leverages a natural language prompt template, a concise sentence that guides the model to perform aspect category identification subtask and curtails the need for data augmentation. The two subtasks are jointly trained in pre-trained language models (PLMs) to capture their correlation. Further, the attention mechanism for aspect categories enables the model to concentrate selectively on significant features such as phrases and words during the predictions. In addition, the verbalizer employs a set of parameters to balance the weight of each label word while projecting between the label space and the label words space. Through experiments on four datasets, our model demonstrated remarkable performance in detecting category-sentiment pairs.

Accumulating evidence for myriad alternatives: Modeling the generation of free association.

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models' huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people's associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

WordIllusion: An adversarial text generation algorithm based on human cognitive system

Although natural language processing technology has shown strong performance in many tasks, it is very vulnerable to adversarial examples, i.e., sentences with some small perturbations can fool AI models. Current adversarial texts for English are usually generated by finding substitute words in adjacent spaces of keyword vectors. Unlike English, Chinese is more discrete and has a more complex font structure, which words that are closer in vector spaces may differ greatly in physical structure. Therefore, adversarial examples generated by current methods possess lower quality and can be easily perceived by human, or rather, they are not suitable for the human cognitive system. In this paper, we propose the “WordIllusion”, a new detectable black-box algorithm used for generating Chinese adversarial texts. In this method, we create a CKSF evaluation indicator to select the key words of sentences. And then, based on the shape bias of human cognitive system and the rectification understanding to create replacement spaces of key words. To verify the effectiveness of WordIllusion, we experiment with two types of text classification tasks by using six natural language processing models. The result indicates that our method is able to improve the accuracy rate efficiently, and the generated adversarial texts can be very misleading.