A performance-driven hybrid text-image classification model for multimodal data by Swati Gupta, Bal Kishan

“…These models center on a complex deep learning architecture including VGG16 for image classification and Roberta with MYSQL for text classification and optimized CNNs. Roberta is very good at extracting valuable information from both textual embeddings and at capturing complex patterns across many modalities. …”
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An Eye-Tracking Study on Text Comprehension While Listening to Music: Preliminary Results by Georgia Andreou, Maria Gkantaki

“…The aim of the present study was to examine the effect of background music on text comprehension using eye-tracking technology. …”
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Linguistic and Cultural Component in Pandemic Coronavirus Discourse (German Humorous Texts and Metaphors) by N. A. Trofimova, Ya. A. Manukyan

“…This article is devoted to the linguocultural study of humorous texts/metaphors which appeared in German during the COVID-19 pandemic. …”
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Arteries and Veins or Bowels and Vessels – On Lexical Fixedness in the Eighteenth-Century Medical Texts by Magdalena Bator, Marta Sylwanowicz

“…The proposed paper aims to conduct a corpus-driven examination of the eighteenth-century English vocabulary used in various types of medical texts such as treatises, recipes, regimens, surgical texts, etc It is argued that formulaicity in medical jargon depended on the text type We focus on binomials, defined as “words or phrases belonging to the same grammatical category having some semantic relationship and joined by some syntactic device such as ‘and’ or ‘or’” (Bhatia 1993: 108) Apart from the investigation into the correspondence between the use of binomials and the text type, the analysis involves exploring their frequency, origin, the type of relationship between their components, and the (ir) reversibility of these elements The study delves into the purpose of using binomials in the examined texts and addresses such key questions as: to what extent was the medical language of the eighteenth century formulaic, whether lexico-syntactic patterns of binomials were repeated across particular types of texts, and whether there was any stability in the structure of the phrases The material used for the analysis is the electronic corpus of Late Modern English Medical Texts (LMEMT, Taavitsainen and Hiltunen 2019), containing a representative collection of texts (of over two million words) from a wide range of eighteenth-century medical writings We adopt the typology and methodology used by Kopaczyk (2013), Mollin (2014), and Kopaczyk and Sauer (2017).…”
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Software engineering meets legal texts: LLMs for auto detection of contract smells by Moriya Dechtiar, Daniel Martin Katz, Hongming Wang

“…Porting well-known code smells principles to various legal contracts, we introduce ”contract smells,” text patterns that are indicative of potentially significant issues within contractual agreements. …”
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Rumor detection using dual embeddings and text-based graph convolutional network by Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy, Arif Ahmed Sekh

“…Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. …”
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Text analysis of rehabilitation nursing policy in China from the perspective of policy tools by Wenjia Li, Yuhan Zhu, Xuehua Zhu

“…Utilizing this two-dimensional analytical framework, researchers classified and coded relevant policy texts that met established criteria. Subsequently, they analyzed their content and performed a quantitative assessment regarding both quantity and distribution patterns.ResultsA total of 41 policy documents were analyzed, resulting in the extraction of 292 policy terms, and in dimension X, environment-based, supply-based, and demand-based policy instruments comprised 46.58, 36.64, and 16.78%, respectively. …”
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Efficient knowledge graph to text powered by LLGM: linear latent graph model by Xiaokang Zhao, Yao Zheng, Yubo Shan, Jingyuan Li, Kun Zhang, Yuanzhuo Wang

“…LLGM’s core innovations are three-fold: (1) a Multi-head Statistical Attention (MSA) mechanism that achieves linear O(N) complexity by replacing pairwise token interactions with efficient statistical approximations, drastically reducing the primary computational bottleneck; (2) a Graph Latent Self-Attention (GLSA) module that efficiently encodes explicit graph structures using dimension-reduced intermediate representations, preserving relational fidelity with fewer parameters; and (3) a Graph Periodicity Projector (GPP) that optimizes feed-forward networks by decomposing representations into periodic and non-periodic components, adeptly capturing both regular and unique graph patterns. Experiments on the WebNLG and EventNarrative datasets demonstrate LLGM’s significant contributions: it achieves competitive text generation quality, evidenced by a mere 0.8% BLEU-4 gap to the top model and the highest CIDEr score (4.63) on WebNLG, while requiring 20–37% fewer parameters than leading models. …”
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Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text by Abeer Saad Alsheddi, Mohamed El Bachir Menai

“…The style change detection (SCD) task asks to find the positions of authors’ style changes within multi-authored texts. It has several application areas, such as forensics, cybercrime, and literary analysis. …”
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METAPHORICAL TERMINOLOGY IN ANCIENT TEXTS OF TRADITIONAL CHINESE MEDICINE: PROBLEMS OF UNDERSTANDING AND TRANSLATION by Qiuhua Sun, Irina S. Karabulatova, Jinna Zou, Chen Kuo

“…The purpose of the work is to identify the patterns of the language of these ancient texts in the context of the interpretation presented in them through the hierarchy of ‘state and subjects’. …”
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Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection by Margaryta Prazdnikova

“…This paper explores an approach to predicting heart attack risk based on analysis of text data of medical reports using machine learning. …”
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Linguistic Patterns of Racism in German Football by Amela Ćurković

“…For this purpose, several media texts were selected that describe cases in which some German football players were exposed to racism, such as Jérôme Boateng, Mesut Özil and Gerald Asamoah. …”
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On the Philosophic Meaning of the “Conflict Pattern” Term by B. V. Kabylinskii

“…Methodologically, the work is based on philosophical texts on the themes of patterns, modeling, scientific instrumentation, the phenomenology of Husserl and Heidegger.Results and discussion. …”
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From Text to Context: The Role of Kyai in Shaping Modern Islamic Inheritance Law by Muhammad Nur, Siti Maymanatun Nisa, Tashdieq Ulil Amri Arhamzah, Imaro Sidqi, Doli Witro

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Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks by Cunhe Li, Zian Xie, Haotian Wang

“…In recent years, text classification has found wide application in diverse real-world scenarios. …”
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Musical Manifestations of Textual Patterning in Estonian Regilaul by Janika Oras

“…The article analyses the marking of song text patterns by means of musical devices in the performances of Estonian regilaul. …”
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HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text by Vanita Kshirsagar, Nishant Pachpor, Ashwini Brahme, Ravindra Aapre, Shubhangi Suryawanshi, Digvijay Bhosale

“…Our model uniquely integrates the architectures of CNN, LSTM, and GRU into one framework for capturing more complex patterns in feature representation, accuracy, and robustness. …”
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Embodied emotions in ancient Neo-Assyrian texts revealed by bodily mapping of emotional semantics by Juha M. Lahnakoski, Ellie Bennett, Lauri Nummenmaa, Ulrike Steinert, Mikko Sams, Saana Svärd

“…Here, we utilized this relationship and computational linguistic methods to map a representation of emotions in ancient texts. We analyzed Neo-Assyrian texts from 934–612 BCE to discern consistent relationships between linguistic expressions related to both emotions and bodily sensations. …”
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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework by Muhammad Ali Aslam, Khairullah Khan, Wahab Khan, Sajid Ullah Khan, Abdullah Albanyan, Shabbab Ali Algamdi

“…The BiLSTM network sequentially processes the input, leveraging both forward and backward contextual information to encode the complex syntactic and semantic patterns inherent in Urdu text. An essential contribution of this work is the creation of a large-scale Urdu Paraphrased Corpus (UPC) comprising 400,000 potential sentence pair duplicates, with 150,000 pairs manually identified as paraphrases. …”
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Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM by Vincentius Riandaru Prasetyo, Mohammad Farid Naufal, Kevin Wijaya

“…This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). …”
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