Textual Context Research Articles

XTQA: Span-Level Explanations for Textbook Question Answering.

Textbook question answering (TQA) is the task of correctly answering diagram or nondiagram (ND) questions given large multimodal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans' understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture toward span-level eXplanations for TQA (XTQA). In this article, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence and then use our proposed coarse-to-fine grained explanation extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result mean intersection over union (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (ND: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits. We have released our code in https://github.com/dr-majie/opentqa.

The Identification of Wildlife and Animals in Sumerian:

Identifying specific animals in the Sumerian vocabulary often requires careful examination of textual contexts, alongside bioarchaeological data and representations in Sumerian art, as well as insights from linguistic, cross-cultural, ethnographic, and faunal studies. This article supports the notion that the Sumerian bird tu-gur4/8mušen referred to the European turtle dove. Artistic depictions and avifaunal remains confirm the presence of the turtle dove in early Mesopotamia, and references to tu-gur4/8mušen are common in Ur III texts, particularly in Puzriš-Dagan, where it was kept for elite consumption. Turtle doves, unlike other Columbidaespecies, migrate. This article proposes that the seasonal migration of the turtle dove through Iraq in the early autumn is referenced in the Umma month iti dal, which should be understood as the “month of flying (birds).” The seasonal migration of turtle doves was important for the agricultural calendar and the overall economy of the state. Understanding natural events of this kind, and ensuring the correct timing for agricultural tasks would have been essential for maximizing yields, and the planting of the fields—as described in the subsequent Umma month known as the “month of sowing seed” (iti šu-numun)—would commence only after these birds had passed through the region.

Are Mathematical Objects ‘sui generis Fictions’? Some Remarks on Aquinas’s Philosophy of Mathematics

Abstract This contribution proposes an interpretation of Thomas Aquinas’s philosophy of mathematics. It is argued that Aquinas’s philosophy of mathematics is a coherent view whose main features enable us to understand it as a moderate realism according to which mathematical objects have an esse intentionale. This esse intentionale involves both mathematicians’ intellectual activity and natural things being knowable mathematically. It is shown that, in Aquinas’s view, mathematics’ constructive part does not conflict with mathematical realism. It is also held that mathematics’ imaginative reasoning is coherent with Aquinas’s doctrine of formal abstraction and his realistism. It focuses on some of Aquinas’s texts, which it places within their textual and doctrinal context and interprets them in the light of some historical elements.

Reality Show in the context of “Post-Documentary” Culture: Critics of Textual Approach

The purpose of the article is to review important aspects of the critical studies in foreign scientific discourse aimed at textual analysis of reality show as a genre and television format, which helps to reconsider its status as a phenomenon of “post-documentary” culture. The research methodology. The work uses, firstly, critical discourse analysis, which allows us to understand the limitations and counterproductiveness of textual analysis of reality show and helps to reveal the constitutive potential of television and cultural production practices, and, secondly, a cultural approach, which allowed us to identify the media-cultural features of reality show and show them as an element of “post-documentary” audio-visual culture in the XXI century. Scientific novelty of the research lies in the fact that for the first time, through the prism of criticism of the textual approach, the peculiarities of reality show as a phenomenon of “post-documentary” audio-visual culture are revealed. Conclusions. It is proved that the counterproductiveness of the textual approach to the conceptualisation of reality show as a genre, since its production contradicts the general logic and the established perception of what a text should be. This genreform has more common with no certain genre traditions or textual features, but with the “processing of reality” at the intersection of fact and fiction, shifting attention from the text to its production. The proliferation of reality TV programmes illustrates the industry’s dependence on “reality” as a tool for advertising marketing and the logic of cultural production. It is affirmed that during conceptualising TV reality texts, it is necessary to pay attention to a number of industrial and market conditions and needs, which prioritises textual production practices and contexts that shape practical decisions, and allows for empirical access to the evolving and mutating genreform of reality show.

Comparative Study of GPT-4Vision and Convolutional Neural Networks in Histopathological Image Analysis

Abstract Introduction/Objective Recent advancements in artificial intelligence (AI), particularly with Large Language Models like ChatGPT-4Vision (GPT-4V), have expanded the potential for automated interpretation of medical images. This study evaluates the diagnostic accuracy of GPT-4V in histopathological analysis of neurodegenerative diseases and compares its performance with traditional Convolutional Neural Networks (CNNs). Methods/Case Report We utilized 1515 histopathological images, including hematoxylin and eosin (H&E) staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease, progressive supranuclear palsy, and corticobasal degeneration. GPT-4V’s performance was assessed through multi- step prompts to assess how textual context influences the image interpretation. In the quantitative analysis, the diagnostic accuracy of GPT-4V and the CNN-based model YOLOv8 was assessed for classifying three tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes. The analysis used both zero-shot and few-shot learning methods, where GPT-4V and YOLOv8 were evaluated on their ability to distinguish these lesions using ten hold-out test images and 490 training images per lesion. Results (if a Case Study enter NA) GPT-4V accurately recognized Lewy bodies in all cases, but it showed limited accuracy in identifying neurofibrillary tangles (20%) and Pick bodies (0%) on H&E slides. Notably, GPT-4V often suggested Alzheimer’s disease as a potential diagnosis, relying more on contextual clues, such as the prevalence of certain diseases, rather than direct image analysis. In the tau immunohistochemical analysis, GPT-4V’s diagnoses were strongly influenced by additional information about staining and brain region. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities from 40% accuracy with zero-shot learning to 90% with few-shot learning, reaching best accuracy with 20-shot learning. This performance compared favorably against YOLOv8, which also achieved 90% accuracy but required 100-shot learning. Conclusion While GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is particularly promising for neuropathology, where acquiring extensive labeled datasets is challenging. The findings highlight the need for ongoing refinement of AI applications in pathology, emphasizing balanced integration of textual and visual data to optimize diagnostic efficacy and reliability.

Code-Switching in Translation: Linguistic Analysis of Multilingual Texts and their Translations

This research explores the occurrence of code-switching in translation, focusing on instances where translators switch between languages within the same textual context, both intentionally and unintentionally. This study investigated the influence of linguistic factors such as language structure, semantic complexity, and intended communicative purpose on code-switching. The analysis employs qualitative methods to identify the strategies and challenges that translators face in managing code-switching. The study reveals that code-switching significantly contributes to semantic and cultural nuances in multilingual texts, emphasizing its critical role in translation accuracy and reader comprehension. The findings offer theoretical understanding and practical guidance for translation practice, enhancing the framework of multilingual translation studies. This research highlights the importance of code-switching in preserving the integrity and richness of multilingual communication in translated works. This study was conducted through the author's personal efforts without any external funding or grants.

Exploring associations between urban soundscape and contextual factors based on a textual data-driven analysis and mapping approach: A case study in Daejeon, Korea

In soundscape research, it is crucial to understand how contextual factors are associated with people’s perception of acoustic environments. This study proposes a Textual Data-driven Context Analysis and Mapping Method (TD-CAMM), aiming at comprehensively exploring the intricate relationship between soundscape and contextual factors. Through a case study employing TD-CAMM, we demonstrate its effectiveness in quantitatively analyzing text-based contextual data, facilitating the identification of patterns and interrelations among diverse contextual elements via statistical analyses. Utilizing collected contextual data including the purpose of visit, time of use, sound source, emotion, and perceived affective quality, we conducted a hierarchical cluster analysis to characterize soundscapes of the selected locations, revealing four distinct clusters. Subsequently, association rule analysis was performed to identify unique associations among contextual elements shaping the soundscapes within each cluster. Cluster 1, the 'Nightlife Zone of Noisy Urban Settings' was characterized by 'Restless' and 'Noisy' emotions associated with nighttime activities. Cluster 2, the 'Lively Human-Centered Zone for Day and Evening Activities' reflected vibrant human interactions. Cluster 3, identified as 'Restless and Noisy Urban Traffic' was marked by transportation sounds, emphasizing urban bustle. Lastly, Cluster 4, the 'Tranquil Urban Nature Walk' encompassed serene natural spaces with 'Tranquil' emotions amidst natural sounds. These findings present a comprehensive approach to evaluating urban soundscapes, with potential applications in future urban soundscape research and design.

Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day Questions.

Background GPT-4V (GPT-4 with vision, ChatGPT; OpenAI) has shown impressive performance in several medical assessments. However, few studies have assessed its performance in interpreting radiologic images. Purpose To assess and compare the accuracy of GPT-4V in assessing radiologic cases with both images and textual context to that of radiologists and residents, to assess if GPT-4V assistance improves human accuracy, and to assess and compare the accuracy of GPT-4V with that of image-only or text-only inputs. Materials and Methods Seventy-two Case of the Day questions at the RSNA 2023 Annual Meeting were curated in this observer study. Answers from GPT-4V were obtained between November 26 and December 10, 2023, with the following inputs for each question: image only, text only, and both text and images. Five radiologists and three residents also answered the questions in an "open book" setting. For the artificial intelligence (AI)-assisted portion, the radiologists and residents were provided with the outputs of GPT-4V. The accuracy of radiologists and residents, both with and without AI assistance, was analyzed using a mixed-effects linear model. The accuracies of GPT-4V with different input combinations were compared by using the McNemar test. P < .05 was considered to indicate a significant difference. Results The accuracy of GPT-4V was 43% (31 of 72; 95% CI: 32, 55). Radiologists and residents did not significantly outperform GPT-4V in either imaging-dependent (59% and 56% vs 39%; P = .31 and .52, respectively) or imaging-independent (76% and 63% vs 70%; both P = .99) cases. With access to GPT-4V responses, there was no evidence of improvement in the average accuracy of the readers. The accuracy obtained by GPT-4V with text-only and image-only inputs was 50% (35 of 70; 95% CI: 39, 61) and 38% (26 of 69; 95% CI: 27, 49), respectively. Conclusion The radiologists and residents did not significantly outperform GPT-4V. Assistance from GPT-4V did not help human raters. GPT-4V relied on the textual context for its outputs. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Katz in this issue.

Initialisms in Scientific Writing in the 19th and Early 20th Centuries

This paper focusses on the role of initialisms in scientific English articles in the Royal Society Corpus (Fischer et al. 2020; Kermes et al. 2016). The development of scientific initialisms is illustrated with frequency data, a discussion of the evolution of the text topics obtained from topic modelling and an analysis of the development of information-theoretic surprisal values of initialisms in three time spans between 1830 and 1919. The overall frequency and diversity of initialisms for scientific concepts has risen considerably between 1830 and 1919 in the context of the ongoing specialisation of the sciences. Particularly from the 1860s onwards scientific initialisms increasingly become shortcuts for multiword units with wordhood and term status. The surprisal values of scientific initialisms decrease over time as such forms more regularly occur in conventionalised textual contexts and fixed expressions. Overall, the analysis of the RSC texts shows that key developments towards the conventionalisation of scientific initialisms as term formation patterns took place in the transitional period from Late Modern to Present-day English.

Verb Form Recognition and Error Detection in English Articles Using Long Short-Term Memory and Grammar Checks

Error checking of verb forms in English articles is beneficial for learning English and improving the fluency of English texts. In this study, long shortterm memory (LSTM) was used to recognize the types of errors in verb forms. To maximize the utilization of textual context information, a bidirectional LSTM algorithm was employed. Simulation experiments were then conducted, and the algorithm was evaluated against the support vector machine (SVM) algorithm and the grammar rules-based algorithm. The bidirectional LSTM method demonstrated higher accuracy in recognizing the parts of speech of words and the types of verb form errors in the text. Additionally, the accuracy was more stable when faced with different types of verb form errors.

Leading-Edge Sentiment Analysis: A Survey of Application Context, Challenges and Advanced Techniques

Background: Data is rapidly expanding in today's digital age. The reason for the expansion of data is due i to social media sites. The Internet produces an enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions, feelings, attitudes, impressions, etc., concerning subjects, products, and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis. Aim: This study provides a thorough examination of the differences between methods of sentiment analysis, as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses, examines its challenges, and outlines common methods of conducting it. Objective: The objective of the current overview is to better understand the market, gauge public opinion, and make strategic decisions. In addition, enterprises, governments, and scholars can all benefit from conducting a sentiment analysis. Methods: In this research, we review and categorize the most widely applied methods of deep learning and machine learning for analyzing sentiment. From the paper, we learn that which sentiment analysis technique is the best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure, traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context, as well as data volume. Results: Regardless of the fact that the English language has traditionally been the focus of research in this field, other spoken languages have recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently, it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases and addressing the problems with Natural language processing that have been stated in the context of sentiment examination. Conclusion: The difficulties of sentiment analysis are examined as well, with the goal of illuminating potential solutions.

Attribute-Driven Filtering: A new attributes predicting approach for fine-grained image captioning

Fine-grained image captioning with attribute information has garnered significant attention in the realms of computer vision and natural language processing, demanding precise and contextually relevant descriptions of visual content. While previous attribute-driven image captioning models have shown improvements, challenges remain, such as the independence of attribute predictors and caption generators and the semantic gap between images and attributes. Another common issue is the inclusion of all attributes at every time step, despite most attributes being irrelevant to the word currently being generated. This can divert the model’s attention toward erroneous semantic details, resulting in a performance decline. To address these issues, we propose a novel Attribute-Driven Filtering (ADF) captioning network designed to provide rich and nuanced descriptions. This model incorporates a unique Attribute Predictor Module (APM) that dynamically predicts the most pertinent attributes in accordance with the textual context, utilizing different attributes at various time steps. The novelty of this approach lies in recognizing that not all attributes hold equal relevance at each time step, and the APM filters out irrelevant attributes to generate precise and contextually relevant captions. Furthermore, this model features a fusion mechanism that integrates visual information from a conventional attention module with attribute information predicted by the APM, aiming to reduce the visual semantic gap between images and attributes. Extensive experimentation demonstrates that the ADF model outperforms advanced models, achieving impressive CIDEr-D scores of 72.0 (Flickr30K) and 123.3 (MS-COCO) through reinforcement learning optimization. It consistently surpasses baseline models across diverse evaluation metrics, highlighting its effectiveness and robustness.

From text to multimodal: a survey of adversarial example generation in question answering systems

AbstractIntegrating adversarial machine learning with question answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems. This article aims to review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. We examine the techniques employed through systematic categorization, providing a structured review. Beginning with an overview of traditional QA models, we traverse the adversarial example generation by exploring rule-based perturbations and advanced generative models. We then extend our research to include multimodal QA systems, analyze them across various methods, and examine generative models, seq2seq architectures, and hybrid methodologies. Our research grows to different defense strategies, adversarial datasets, and evaluation metrics and illustrates the literature on adversarial QA. Finally, the paper considers the future landscape of adversarial question generation, highlighting potential research directions that can advance textual and multimodal QA systems in the context of adversarial challenges.

A Unified Framework for Analyzing Textual Context and Intent in Social Media

A Unified Framework for Analyzing Textual Context and Intent in Social Media

Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model.

Recent advances in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)-a derivative feature of ChatGPT-have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT-4V in image classification tasks of histopathological images and compares its performance with a traditional convolutional neural network (CNN). We utilised 1520 images, including haematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer's disease (AD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We assessed GPT-4V's performance using multi-step prompts to determine how textual context influences image interpretation. We also employed few-shot learning to enhance improvements in GPT-4V's diagnostic performance in classifying three specific tau lesions-astrocytic plaques, neuritic plaques and tufted astrocytes-and compared the outcomes with the CNN model YOLOv8. GPT-4V accurately recognised staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few-shot learning markedly improved GPT-4V's diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning, matching the performance of YOLOv8, which required 100-shot learning to achieve the same accuracy. Although GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its performance. This approach is especially promising for neuropathology, where acquiring extensive labelled datasets is often challenging.

Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval

Vision-Language Pretraining (VLP) and Foundation models have been the go-to recipe for achieving SoTA performance on general benchmarks. However, leveraging these powerful techniques for more complex vision-language tasks, such as cooking applications, with more structured input data, is still little investigated. In this work, we propose to leverage these techniques for structured-text based computational cuisine tasks. Our strategy, dubbed VLPCook, first transforms existing image-text pairs to image and structured-text pairs. This allows to pretrain our VLPCook model using VLP objectives adapted to the structured data of the resulting datasets, then finetuning it on downstream computational cooking tasks. During finetuning, we also enrich the visual encoder, leveraging pretrained foundation models (e.g. CLIP) to provide local and global textual context. VLPCook outperforms current SoTA by a significant margin (+3.3 Recall@1 absolute improvement) on the task of Cross-Modal Food Retrieval on the large Recipe1M dataset. We conduct further experiments on VLP to validate their importance, especially on the Recipe1M+ dataset. Finally, we validate the generalization of the approach to other tasks (i.e, Food Recognition) and domains with structured text such as the Medical domain on the ROCO dataset. The code will be made publicly available.

Robust Commonsense Reasoning Against Noisy Labels Using Adaptive Correction.

Commonsense reasoning based on knowledge graphs (KGs) is a challenging task that requires predicting complex questions over the described textual contexts and relevant knowledge about the world. However, current methods typically assume clean training scenarios with accurately labeled samples, which are often unrealistic. The training set can include mislabeled samples, and the robustness to label noises is essential for commonsense reasoning methods to be practical, but this problem remains largely unexplored. This work focuses on commonsense reasoning with mislabeled training samples and makes several technical contributions: 1) we first construct diverse augmentations from knowledge and model, and offer a simple yet effective multiple-choice alignment method to divide the training samples into clean, semi-clean, and unclean parts; 2) we design adaptive label correction methods for the semi-clean and unclean samples to exploit the supervised potential of noisy information; and 3) finally, we extensively test these methods on noisy versions of commonsense reasoning benchmarks (CommonsenseQA and OpenbookQA). Experimental results show that the proposed method can significantly enhance robustness and improve overall performance. Furthermore, the proposed method is generally applicable to multiple existing commonsense reasoning frameworks to boost their robustness. The code is available at https://github.com/xdxuyang/CR_Noisy_Labels.

Integration of Narrative Strategies in Poster Design Education for Southeast Asian Students

Owing to the proactive implementation of the New Southbound Policy, the proportion of Southeast Asian students studying in Taiwan is steadily increasing. They exhibit variations in perceptions, communication, and cultural backgrounds in learning. These differences often lead to confusion and challenges during the coursework. In this study, we integrate narrative strategies into poster design education, where professors teach students textual contexts to identify and solve problems, and ultimately integrate design knowledge and skills. We investigated the differences in the student’s performance in poster design after the integration of narrative strategies and their reactions. Using a pre-experimental one-group pretest-posttest design with 40 Southeast Asian students, the 12-week experimental teaching was offered. Significant improvements in creativity, text composition, illustrations, layout, and color in poster design were observed after the period. The students could utilize transformation and metaphor as design techniques, demonstrating that integrating narrative strategies stimulated students’ imaginative capacities. Such a result indicates significant efficacy in enhancing the learning experiences of Southeast Asian students.

Emotional Context of Newspaper Articles about Covid-19

This paper explores the newspaper texts about Covid-19 from the point of view of emotions. New Zealand has been chosen for the country where Coronovirus pandemic was kept at bay for a long time. But after the cases started to escalate, the situation changed and the tone of newspapers changed, too. The analysis showed the dependence of emotions in the texts and the rise of Covid-19 cases as an indicator of danger. The article analyses three phases of the situation in New Zealand with Covid, such as phase 1 when the number of new cases was controlled, phase 2 when the number of new cases was quickly escalating, and phase 3 when the number of new cases was still high but not so quickly changing. The corpora were analyzed from the point of view of negative and positive reaction to the situation of pandemic escalation in the world and in New Zealand. The findings show a strong relation of the social and textual contexts.

Less is More: Stress Detection through Condensed Social Media Contents

In the digital age, social media has been a go-to platform for stress-related discussions, yielding valuable data to advance the understanding and detection of stress. Swift identification of stress indicators in these online conversations is essential in enabling immediate support and helping to avert subsequent severe mental and physical health issues, especially during global crises such as pandemics and conflicts. Detecting stress in social media posts automatically poses a formidable challenge. While techniques such as supervised Pretrained Language Models (PLMs) and zero-shot Large Language Models (LLMs) based classifiers have demonstrated significant performance, they exhibit limitations, especially on platforms like Reddit. For example, on Reddit, users tend to write lengthy, expressive posts, which causes these methods to often fail to consider the entire context, leading to incomplete or inaccurate assessments of a user's mental health or stress status. To overcome these limitations, we present a new approach to identifying and classifying stress-related discourse on social media. Our approach involves analyzing condensed versions of user posts, such as user-provided summaries or the "Too Long Didn’t Read" (TLDR) portion of the original post. We question whether these abridged texts can yield a more accurate classification of stress. In this paper, we make the following contributions. First, we investigate the relationship between the performance of the model's perceived textual context and the length of social media posts. Second, we present a novel approach to use the summarized texts for stress detection. We experiment with different classifiers to evaluate their performance on stress detection accuracy using summarized versus full-length posts. Furthermore, by examining the emotional and linguistic features of the original posts and their summaries, we suggest improvements to current state-of-the-art LLM-based stress classifier prompts, thereby enhancing stress detection capabilities. Finally, when user summaries are absent, we synthetically generate meaningful user post summaries by incorporating the power of LLMs. Our results show that the stress detection performance deteriorates for longer posts, and utilizing the TLDR and summaries improves classification outcomes. We also provide augmented datasets containing human and AI-generated summaries for future research in stress detection on social media.