Text Processing Research Articles

Detection and treatment of string events in the limit

A string event is a pattern that occurs in a stream of characters. The need to detect and handle string events in infinite texts emerges in many scenarios, including online treatment of logs, web crawling, and syntax highlighting. This paper describes a technique to specify and treat string events. Users determine patterns of interest via a markup language. From such examples, tokens are generalized via a semi-lattice of regular expressions. Such tokens are combined into a context-free language that recognizes patterns in the text stream. These techniques are implemented in a text processing system called Lushu, which runs on the Java Virtual Machine (JVM). Lushu intercepts strings emitted by the JVM. Once patterns are detected, it invokes a user-specified action handler. As a proof of concept, this paper shows that Lushu outperforms state-of-the-art parsers and parser generators, such as Comby, BeautifulSoup4 and ZheFuscator, in terms of memory consumption and running time.

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

How does lexical coverage affect the processing of L2 texts?

How does lexical coverage affect the processing of L2 texts?

Cost and benefit of parafoveal information during reading acquisition as revealed by finger movement patterns

Contrary to expert readers, children learning to read have limited ability to preprocess letters in parafoveal vision. Parafoveal letters induce crowding cost: the features of neighboring letters interfere with target letter identification. We longitudinally studied the weight of parafoveal cost and benefit in two group of children (N = 42), during their first school year (Group 1) and at the end of second school year (Groupe 2). Using a novel digit-tracking method, a blurred text was presented and rendered unblurred by touching the screen, allowing the user to discover a window of visible text as the finger moved along it. We compared two conditions: (1) a large window, where crowding was enhanced by the presence of parafoveal information; (2) a small window, where crowding was suppressed by blurred parafoveal information. Finger kinematics were simultaneously recorded. We found that at the beginning of first-grade, digital fixations - brief slowing or stopping of the finger on a specific point - are significantly longer in the large compared to the small window condition, as parafoveal crowding increases text processing difficulty. This effect diminishes and disappears at the end of second-grade as reading performance improves. In the large window condition, longer digital saccades - rapid movements of the finger changing position - appear by the end of first grade suggesting that parafoveal exposure become more beneficial than harmful when children acquire basic reading skills. Our results show that in beginning readers, crowding has a cognitive cost that interfere with the speed of the learning reading process. Our findings are relevant to the field of education by showing that visual crowding in first grade should not be underestimated.

Saudi Learner Translation Corpus: The design and compilation of an English-Arabic learner translation corpus.

This article introduces the Saudi Learner Translation Corpus (SauLTC), an innovative multi-version English-Arabic parallel corpus featuring part-of-speech tagging. We describe the corpus parameters and compilation process and explain how textual processing and sentence alignment are conducted. The participants include 366 student translators, 48 instructors, and 23 alignment verifiers. The corpus provides access to two target versions of every ST to allow the detection of the changes in the translation and revision processes from the initial to the final draft. The translations were collected over three years, yielding 5,160,386 tokens. The metadata of 23 sentence alignment verifiers were added to the analysis as a unique variable to investigate individual differences in the manual verification process. This unidirectional corpus can be used to identify student translators' strategies and errors in translation and analyze the efficacy of instructors' feedback. Furthermore, it is accessible via an application and a website. It provides translation teachers and researchers with a database that can help develop corpus-based and corpus-driven teaching materials.

Efficient labeling of french mammogram reports with MammoBERT

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.

An Extended Pattern Based Comprehensive Stemmer for the Urdu Language

The Urdu language is used by approximately 200 million people for spoken and written communications on a daily basis. There is a substantial amount of unstructured Urdu textual data that is available worldwide. Data mining techniques can be used to extract meaningful knowledge from such a large, potentially informative source of data. There are many text processing systems available to process unstructured textual data. However, these systems are mostly language specific and developed for a variety of languages such as English, Spanish, Chinese, etc. Unfortunately, there are not as many language processing resources available for Urdu. Stemming is one of the most important preprocessing steps in the text mining process and its goal is to reduce grammatical words form, e.g., parts of speech, gender, tense, and so on, to their root form. In this work, we have extended the stemming capabilities of our existing pattern-based comprehensive stemming system for Urdu text. In addition to the existing stemming rules in previous work, we introduce novel stemming rules for prefix, and infix stemming. We also optimize the existing suffix removal rules and extend the add character lists for word normalization. These stemming rules are generic and have the ability to generate the stem of Urdu words as well as loan words (words belonging to other languages i.e. Arabic, Persian, Turkish, etc). In the experimental evaluation, we have observed a significant improvement in the overall stemming accuracy of our proposed pattern-based Urud stemmer, which demonstrates the adoptability of the proposed stemming approach for a variety of text-processing applications.

ConvAtt Network: A Low Parameter Approach For Sign Language Recognition

Despite recent advances in Large Language Models in text processing, Sign Language Recognition (SLR) remains an unresolved task. This is, in part, due to limitations in the available data. In this paper, we investigate combining 1D convolutions with transformer layers to capture local features and global interactions in a low-parameter SLR model. We experimented using multiple data augmentation and regularization techniques to categorize signs of the French Belgian Sign Language. We achieved a top-1 accuracy of 42.7% and a top-10 accuracy of 81.9% in 600 different signs. This model is competitive with the current state of the art while using a significantly lower number of parameters.

The Impact and Future of Generative Pre-trained Transformers: A Study of GPT in Enhancing Business and Technology

With the development of artificial intelligence (AI), natural language processing (NIP) has clearly become the key to enhancing AI capabilities, especially through the deployment of GPT. This paper explores the major advances and related commercial applications of GPT, as it is at the cutting edge of AI technology with far-reaching capabilities in terms of text generation, language understanding, and human-machine interaction. Developed by OpenAI, GPT uses innovative transformer architecture to make breakthroughs in a variety of language tasks, marking a major evolution in the potential applications of AI in various industries. This research explores the integration of GPT in sectors, highlighting how its complex text processing capabilities can be utilized to improve efficiency and innovative service delivery. The study highlights the transformative impact of GPT on business efficiency, strategic decision-making, and customer engagement, thanks to its scalable and adaptable AI framework. Finally, the document reflects the need for a strong ethical framework and regulatory supervision to guide the deployment of GPT technologies. As the Global Trade Agreement evolves, it is committed to shaping the future of global industry by promoting innovation while calling for a balanced approach to ethical considerations and social impacts.

Automatic Processing of Bibliometric Data: Newest Literature Analysis Algorithms

Automated processing of bibliometric data has become an integral element in understanding and measuring the impact of scientific research as well as trends in the academic literature. The purpose of this study was to evaluate and compare current literature analysis algorithms to identify advances in automated processing of bibliometric data. This study explores the use of the latest algorithms in literature analysis which includes identification of research themes, research network analysis, citation impact evaluation, and development of literature recommendation algorithms. This research exposes innovative applications that utilize the latest technologies in text processing and data analysis to assist researchers, libraries, and information professionals in revealing deep insights from the latest scientific literature. The results of this study illustrate that the automated processing of bibliometric data with the latest algorithms paves the way for a deeper understanding of the development of scientific research, its impact in the scientific community as well as improving bibliometric analysis methods and tools by integrating the latest algorithms, so that scientific research and trends can be identified more accurately and relevantly. The study could use more sophisticated multifaceted techniques to analyze simple productivity measures based on article citations. This research is also able to recognize and describe the development of new science and technology.

Economic aspects of error identification in semi-structured publications in the state language

Due to the rapid growth of information on the Internet and social networks, research in the field of computational linguistics has become very relevant. The volume of information that people and machines create in natural language needs to be processed, analyzed and verified. Information retrieval systems, dialog systems, and machine translation tools are used for this. The range of automatic text processing systems is very wide, it covers various tasks. Finding errors in texts and words, identifying and correcting incorrect words is one of the most important tasks of natural language processing (NLP). The article provides an overview of semi-structured data, methods and technologies for identifying incorrect words in natural languages. The paper gives an overview of semi-structured data, methods and techniques for detecting incorrect words in natural languages. The aim of the research is to develop an effective approach for detecting and correcting errors occurring in Kazakh-language texts, especially in the context of limited resources and unstructured data. The research includes the use of machine learning techniques as well as economic analysis of the costs of developing and implementing such solutions. The proposed approach facilitates the automation of text verification, which can significantly reduce the cost of manual data processing and improve the quality of information in various spheres, including business and public administration.

Automatic closed captions and subtitles in academic video presentations: possibilities and shortcomings

In light of the increasing number of academic events being recorded or held online since the onset of the COVID-19 pandemic, the present work combines automation processes in audiovisual translation and academic texts–more specifically, video presentations. The research questions are whether the automatic generation of captions is functional to ensure accessibility in academic events and how much post-editing effort would such content require in case a machine translation of the subtitles is to be applied. The research method comprises several phases. First, in a corpus of video presentations of specialised content in English, captions were generated automatically using YouTube Studio to ascertain the general quality and the type of errors generated in the automatically generated closed captions according to Multidimensional Quality Metrics (MQM) framework. These auto-generated captions were corrected and annotated by considering the following parameters: a) pre-editing time, b) type of error according to MQM framework, and c) severity of the error. Second, the auto-generated captions and corrected were machine translated into Spanish. Furthermore, errors detected in the machine translation of the subtitles (English-Spanish) were post-edited and errors were analysed following the MQM. Reception by a potential audience was also studied, as evaluated by academics from the same field of expertise. The main conclusion is that most errors in machine-translated subtitles stem from incorrect caption segmentation and lack of context awareness, making it essential to correct the closed captions before translation. This thesis is supported by the reception study in which the level of comprehension was higher when the transcription was pre-edited, as most of the problems arise from the closed captions rather than from the translation itself.

Интеллектуальная обработка текстовой информации: обзор автоматизированных методов суммаризации

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.

Новообразования в публицистических текстах региональных СМИ на эрзянском языке: словообразовательный аспект (на материале журнала «Сятко» / «Искра»)

The aim of the research is to identify the lexical peculiarity and word-formation processes in journalistic texts of regional mass media. The paper examines neologisms, lexical units created based on the internal resources of the regional (Erzya) language. The work emphasizes the important role of the media in preserving, enriching, and transmitting native languages. The scientific novelty of the research lies in conducting a structural and semantic analysis of neologisms, which, based on extensive linguistic material, allowed for identifying and characterizing the main productive word-formation models and methods of word formation in the modern language of the media in Erzya. As a result of the research, it was found that the most productive methods of word formation include suffixation, root affixation, compounding, and substantivization. It was also noted that the neologisms identified from journalistic texts perform a stylistic function of influencing the reader (listener) due to their rich emotional-expressive charge and wide semantic content.

Medical Data NER and Classification Using Hybridized BERT Model

The extraction of important information from medical texts by Named Entity Recognition (NER) is a key component of advanced medical text processing. Medical practitioners rely heavily on NER's assistance with disease surveillance, clinical resolution building, and substantiation-based treatment. As the foundation of text information processing in the medical field, it guarantees precise location of data required for knowledgeable medical decisions and attentive disease surveillance. Additionally, a core goal in medical Natural Language Processing (NLP) is medical text categorization, which tries to classify short medical texts into distinct groups. Most recent work has concentrated on using pre-trained linguistic processes for text cataloging in medicine. The present work presents a novel clinical neural network architecture (NER) method that was created with a customized Rule Based BiLSTM-BERT (Bidirectional Encoder Representations from Transformers) architecture that incorporates Retrieval Augment Generation. Across several fields, including medicine, deep learning has demonstrated noteworthy advancements. These results show that, when applied to our test dataset, the BiLSTM-BERT-RAG model produced results that were almost human-like. The system proficiently recognized pertinent vocabulary representative of the intended protocol.

Language models outperform cloze predictability in a cognitive model of reading.

Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye-movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT-2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements.

Türkçenin Yabancı Dil Olarak Öğretiminde Okuma Metinlerinin Kültür Aktarımında Kullanımı ve Metin İşleme Sürecine Uygunluğu

In foreign language teaching, not only grammar but also cultural teaching should be targeted. It is important that the selected texts facilitate both language learning and cultural learning and make students love language teaching. In order to teach Turkish and Turkish culture, students should read texts that describe Turkish culture and convey the cultural elements in these texts to others in written and oral form, which allows them to know and embrace these cultural elements. The texts used should include cultural elements and should be aimed at teaching Turkish culture to students. Reading texts that accurately convey Turkish culture should be included in teaching Turkish to foreigners. Because each language is taught within its own logic. The purpose of this research is to address the importance and necessity of using texts for cultural transfer. In this direction, a case study was conducted within the framework of qualitative research and the research was presented. In the formation of the research, the texts named Wedding in Anatolia, Turkish Bracelets, Sultan Ahmet Mosque and Hagia Sophia were selected and examined in terms of cultural transfer during the teaching of Turkish to foreigners. As a result of the research, it was determined that students who read the selected texts and learn Turkish as a foreign language will be introduced to Turkish culture, customs and traditions. Key words: Teaching Turkish as a foreign language, Turkish language, text usage, cultural transfer, achievements, processing of text process

Few-Shot Image Classification of Crop Diseases Based on Vision-Language Models.

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision-Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure.

Comparative analysis of Russian language textbooks for primary school using quantitative methods

Despite the intense scientific and practical interest in assessing the textual content quality of a school textbook, there is still no transparent algorithm and objective criteria for such evaluation. This article aims to outline objective linguistic criteria for textbook text assessment using quantitative methods on the example of one of the main components of a Russian language textbook, i. e. exercise texts. The analysis was performed on the material of 8 lines of modern Russian language textbooks for primary school and 1 line of the digital educational service Yandex Textbook. The research employed the methods of automatic natural language processing, statistics, and corpus analysis of texts. As a result, we propose an algorithm for analysing the text library of a Russian language textbook according to the criteria of structural and lexical complexity, genre, stylistic and thematic diversity, and the degree of correspondence between the text library and the current state of the Russian language. The obtained results form an idea of the textual content of a modern Russian language textbook for primary school. Moreover, they highlight the specific features of text selection for modern traditional and digital Russian language textbooks and resources. Finally, the research outcomes raise questions concerning the methodological optimality of certain authorial decisions when compiling the text library of a textbook. The results of the study will be useful to authors of school textbooks on the Russian language and specialists in educational and methodological work.

Cascaded Adaptive Dilated Temporal Convolution Network-Based Efficient Sentiment Analysis Model from Social Media Posts

Sentimental analysis is one of the most complicated tasks in text mining. It is the process of converting unstructured text to structural text to identify the meaningful pattern of data. Different kinds of sentiments in social media posts are classified into positive and negative. Natural Language Processing (NLP) plays an imperative task in examining sentiments on social media. Sentimental analysis techniques frequently use text data to help to monitor customer feedback about products and understand the needs of customers in the field of business. The text data are gathered from social media comments that lead to several drawbacks such as spelling errors, noise, and unstructured data, and some of the data are in abbreviation form in which abbreviations are hard to understand from the text data. Therefore, the innovative sentiment analysis method is implemented to identify the kinds of sentiments from the collected social media posts. At first, the text data are assembled from the social media posts. The collected text data are subjected to text preprocessing techniques to improve the data quality. NLP techniques such as a bag of n-grams, glove embedding, and Bidirectional Encoder Representations from Transformers (BERT) are applied to separate the features from the text data. The removed features from these techniques are given to the fusion of the optimal feature stage, where the weight optimization takes place via the Mutated Random Parameter-based Serval Optimization Algorithm (MRP-SOA). The attained optimal weighted fused features contain the most relevant information, and it is fed to the classifier for analyzing the sentiments. For analyzing the sentiments, the Cascaded Adaptive Dilated Temporal Convolutional Network (CADTCN) is utilized, and the parameters from ADTCN are optimized with the same to get higher classification outcomes. The analysis outcomes are compared with the traditional sentiment analysis models in terms of various metrics to show the success of the advanced design.