Language Data Research Articles

Building language and genre awareness through learner corpus data in a second language writing course

Abstract This study is part of a larger project investigating the impact of corpus-based teaching in a series of second language (L2) genre-based writing courses. In this paper, we focus on activities that were implemented in one L2 writing course between first and final drafts of two major assignments (a Literacy Narrative and a Genre Analysis). Materials were created using Crow (Corpus and Repository of Writing; https://crow.corporaproject.org) and designed to interact with an existing genre-based curriculum. Findings show modest but clear changes by students who interacted with the corpus-based materials. We also discuss our use of learner corpora, which departs somewhat from previous literature, including an asset-based approach where learners’ texts are used as models for other learners.

Ukrainian biaspectuality: An instantiation of compositional aspect in a verbal-aspect language

Aspect, the perfective-imperfective contrast, is a universal phenomenon, part of man’s cognitive organization to reflect objective/subjective reality by conceptualizing referents of verbs and of nominals/NPs standing for participants in situations as temporal entities, residing in speaker-hearers’ heads and interacting between each other. Aspect is instantiated across languages through two archetypes: verbal aspect (VA) – grammatical, as in the Slavic languages, including Ukrainian; compositional aspect (CA) – complex semantico-syntactic, sporadically dependent on pragmatic discourse elements, as in English. The paper explores Ukrainian language data to, first, confirm that CA, realized mainly as a very complex interplay of sentence components, exists not only in CA languages but, albeit peripherally, also in VA languages, including Ukrainian. Second, to find out how the Ukrainian aspect is realized in sentences with biaspectual verbs and particular numbers of situation-participant NPs: three, two, one. The referents of verbs and of nominals/NPs standing for participants in situations in both VA and CA languages are part of the never-ending process of thinking and perpetual resorting to memory and are not some abstract self-contained system of symbols divorced from human cognition. Phrased otherwise, aspect, especially CA, cannot be understood within the domain of traditional grammar and mainstream linguistics with their naivist notions ignoring man’s cognitive capacity and maintaining, inter alia, that nominals/NPs are concrete/physical or abstract entities. The study of matter is ordained to physics. Linguistics must investigate not the material world but how language reflects this world and other possible (imaginable) worlds. A simple analogue is a woman in a mirror: it is not a material object but an image of a woman; likewise, a woman referred to through language is not a material object but a token of a woman. Hence, NP referents of material things are not physical entities but images of such entities, fully describable, and their kineticism is handled by verb referents, whereby the intricate CA mechanism, which is cognitive, can be observed, albeit peripherally, also in VA languages, including Ukrainian. Disclosure Statement The authors reported no potential conflicts of interest. * Corresponding author: Krasimir Kabakčiev, 0000-0002-5529-0872kkabakciev@atiner.gr

Proficiency-rated learner corpora

Abstract In this position paper, I argue that proficiency-rated learner corpora should gain a more prominent role in data-driven learning (DDL). With specific reference to DDL, proficiency-rated learner corpora can provide typical, atypical and erroneous target language data at different levels of proficiency, which can be meaningfully used in the design of learning activities. This makes them pivotal in expanding the scope of DDL to include mid- and lower-level proficiency learners more extensively. Although the field of learner corpus research has been promoting learner corpus use in DDL for a long time, only a small fraction of DDL studies make use of a learner corpus. As a contribution to overcome this hiatus, I will demonstrate how using a specific proficiency-rated learner corpus (i.e., the CELI corpus; Spina et al., 2022, 2023) can enrich the design of DDL activities, making them more adaptable to a wider range of learner needs.

Dominant Language, Urbanization and Lexical Depletion of Native Language: A Corpus Linguistics Study

India is home to hundreds of languages. Each Indian language is unique and an identity marker for individuals, communities, and their culture. The English language, though not native to India, has a significant presence in this vast country. One hundred and ninety years of British colonial rule over India had established English as the ‘dominant language’ in the country long ago. Rapid urbanization; and the consequent proliferation of telecommunication networks have further increased its dominance over Indian languages in the last few decades. The heavy code-mixing of English words in the Indian languages is reducing the richness of the vocabulary of the Indian languages by replacing their words with English words, thereby initiating ‘lexical depletion’ based language change in the native languages. This paper is an empirical study to map the code-mixing of English words in the Hindi language and its impact on the Hindi language, as observed in Greater Noida, a town that has seen rapid urbanization since 1991. This paper attempts to explain the implications of code-mixing on the Hindi lexicon by analysing a corpus of spoken language data using corpus-linguistics and computational linguistics methodologies.

BERTopic for Enhanced Idea Management and Topic Generation in Brainstorming Sessions

Brainstorming is an important part of the design thinking process since it encourages creativity and innovation through bringing together diverse viewpoints. However, traditional brainstorming practices face challenges such as the management of large volumes of ideas. To address this issue, this paper introduces a decision support system that employs the BERTopic model to automate the brainstorming process, which enhances the categorization of ideas and the generation of coherent topics from textual data. The dataset for our study was assembled from a brainstorming session on “scholar dropouts”, where ideas were captured on Post-it notes, digitized through an optical character recognition (OCR) model, and enhanced using data augmentation with a language model, GPT-3.5, to ensure robustness. To assess the performance of our system, we employed both quantitative and qualitative analyses. Quantitative evaluations were conducted independently across various parameters, while qualitative assessments focused on the relevance and alignment of keywords with human-classified topics during brainstorming sessions. Our findings demonstrate that BERTopic outperforms traditional LDA models in generating semantically coherent topics. These results demonstrate the usefulness of our system in managing the complex nature of Arabic language data and improving the efficiency of brainstorming sessions.

An integrated model based on deep learning classifiers and pre-trained transformer for phishing URL detection

The unique nature of website URLs has made phishing detection a challenging task. Unlike natural language, URLs have an unstructured nature with non-linear and sophisticated correlations. Therefore, they should be handled as both natural language and unstructured data sequences. However, the current solutions for phishing URL detection only focused on a single aspect of web page URLs. In this concern, this paper proposes an integrated model based on DL classifiers and pre-trained transformer to examine both the unique nature and the natural language structure of URL sequences simultaneously. The proposed model consists of three modules: RasNet (Keras-ResNet), TCMA (TCN-MHSA), and MPNet (Masked and Permuted Pre-training for Language Understanding). Considering the unique nature of the input data, RasNet combines two Keras embedding techniques to obtain the feature representations of URLs and then fuses them using a Residual Network (ResNet) to balance the weight distribution among the character-level and word-level information. Additionally, TCMA integrates the Temporal Convolutional Network (TCN) with the Multi-Head Self-Attention (MHSA) mechanism to optimize feature extraction and improve classification accuracy. Concurrently, MPNet joins the advantages and eliminates the drawbacks of Masked Language Modelling and Permuted Language Modelling to examine the nature language structure of web page URLs. The proposed model was trained and tested on four different datasets, including Ebbu2017, PhishCrawl, 420K-PD, and 1M-PD. The experimental results indicated that the proposed solution outperformed other models in classifying malicious URLs with the highest detection rate of 99.71% on the 1M-PD dataset, improving the performance accuracy of the state-of-the-art approaches by 1.37% to 2.01%.

Digital footprints as means of measuring loneliness experience and embeddedness in social networks for designing digital mental health interventions

Introduction & BackgroundDespite existing research evidence for the negative influence of loneliness on people’s wellbeing, most studies focus on the experiences of older adults and the student population. Moreover, research concerning loneliness and digital footprints uses demographic proxies, as opposed to a behavioural focus, thus providing an incomplete representation of the phenomenon’s influence on the general population. Objectives & ApproachThis project aims to use people’s digital social media data (shared by the participants from their Facebook, Twitter, or Reddit accounts) to address people’s experiences of loneliness in order to provide guidance for the design of interventions catering to the improvement of the wellbeing of individuals. Screening the participants for loneliness levels using the UCLA Loneliness Scale and looking how these experiences differ cross-sectionally (25-65-year-olds; minorities) will help understand the following: social network structures, as shaped by loneliness experience; the dynamics within one’s social network; and the linguistic content of the relationships. Using digital footprints for language modelling and thematic analysis of digital language data shared by the participants, in addition to social network analysis (mapped out based on the individuals’ digital interactions) will allow insight into digital wellbeing. A traditional approach will be utilised alongside digital data analysis to address the issue of limited social media data representativeness - relationships formed in non-digital settings, along with the associated loneliness experiences, will be included. In addition to sharing their digital footprints, the participants will be surveyed and interviewed about their everyday offline and digital experiences of loneliness; as well as their social network structures and dynamics. The interview and survey data will be analysed using thematic analysis of text data and predictive models of quantitative survey responses; in addition to social network analysis of the relationships listed during the interview. Predictions of loneliness outcomes in relation to people’s digital and offline behaviour; and correlations between loneliness experiences and social network dynamics will be made from the data. Relevance to Digital FootprintsThe project focuses on people’s experiences of loneliness in both digital and offline settings utilising the analysis of digital footprints from social media and traditional survey- and interview-based methodology. This approach will allow to gain insight into the similarities and differences between social network structures and dynamics as well as loneliness experiences in digital and real-world relationships as these inevitably interplay in everyday life. The inclusion of digital footprints data will allow to measure and predict loneliness impact on mental health and digital social behaviour; and design tailored digital wellbeing interventions in the future. Conclusions & ImplicationsThe results will serve as basis for designing innovative loneliness interventions for both the public and counsellors to offer tailored wellbeing support. Additionally, the project aims to promote loneliness awareness. Incorporating a variety of experiences from different social groups will enable inclusive, user-centred approach.

Real-Time Arabic Sign Language Recognition Using a Hybrid Deep Learning Model.

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.

Musical instruments, tools, language and genetic data reveal ancient hunter-gatherer networks.

Musical instruments, tools, language and genetic data reveal ancient hunter-gatherer networks.

Диалектные частицы башкирского языка: семантика и функции

This article is devoted to the functional-semantic description of particles of the Karaidel subdialect of the northwestern dialect of the Bashkir language. As examples, field materials and transcribed audio recordings of the author, collected during an expedition to the Karaidel region of the Republic of Bashkortostan in 2022, 2023, were used in order to study the current state of dialect and analyze the morphological indicators of speakers’oral speech of this area. During the processing of field materials, the frequent use of various particles in the speech of informants was revealed, which convey an emotional attitude towards the events, incidents, state and actions of a particular subject or object under discussion. A comparative study of dialect forms with particles of the literary (written) Bashkir language made it possible to classify them in a semantic aspect. Since in colloquial speech specific dialect particles complement the meaning of a particular lexeme and thus their main function is to express an additional semantic connotation of the word. Based on their origin, particles of the Karaidel subdialect can be divided into original ones, dating back to the ancient Turkic state, and borrowed ones, which entered the Bashkir spoken language from the Russian language during centuries-old language contacts. It is noteworthy that some dialect particles of vernacular are observed in other Turkic languages (Kypchak, Oguz, Karluk, Siberian, etc. groups) and are not the literary norm of the Bashkir language. In addition, the article briefly examines the centuries-old history of the study of particles of the Bashkir language. The academic grammatical tradition, dating back to antiquity, considers particles according to the following meanings, which we have supplemented with new dialect materials: 1. Interrogative particles -мы, -ме, -мо, -мө. These particles of the Bashkir language are always written together. 2. The restrictive particles ғына, генә, ҡына, кенә distinguish this or that object or subject from others and are written separately. 3. Particles of doubt ҙар, -ҙыр, -ҙер, conveying uncertain assumptions, inaccuracy regarding an ongoing event, action, phenomenon, object. It should be noted that in the dialects of the northwestern dialect of the Bashkir language, particles of uncertainty and assumptions have only three variants -ҙар, -ҙыр, -ҙер, in contrast to the literary 4-variant affixation: -ҙар, -ҙер, -лар, -лер, -дар, -дәр, -тар, -тәр. 4. The particles of request -сана, -сәнә, -сы, -се, -со, -сө, әле, expressing exhortation, appear with verbs in the form of the imperative mood. The first two forms are used very rarely; mainly in dialects, requests are expressed in forms in , -сы, -се, -со, -сө and әле. 5. Particles of confirmation with a tinge of surprise and annoyance, expressing the emotional state of the speaker: ҙабаса, ҙәбәсә, даса, дәсә, даса(уы), дәсә(үы), дабаса, дәбәсә, табаса, тәбәсә, лабаса. 6. Intensifying particles уҡ, үк, иң, ныҡ, ҙа, ҙә, да, дә, та, тә, ла, лә enhance the meaning of the information expressed. The constant use of particles with the interdental voiced consonant ҙа, ҙә, conveying additional semantic shades to certain parts of speech, is a specific feature of the Karaidel dialect of the northwestern dialect of the Bashkir language and is observed only in the Bashkir language among other Turkic languages. 7. The next specific function word of this dialect is the affirmative dialect particle ҡый, кей in the meaning ‘after all; So; Yes’. So, dialectal data of the Bashkir language can serve as valuable material for comparative historical studies of Turkic languages, since the dialects of the Bashkir language have morphological facts for comparison with the languages of both the Kipchak and Bulgar, Oguz, Karluk and Siberian groups. Moreover, the dialects of the Bashkir language have many features that distinguish this language from other Turkic languages, so dialect materials can be a source of some important data for clarifying a number of questions on the history of the Turkic languages.

Exploring climate change discourse on social media and blogs using a topic modeling analysis

Climate change is one of the most pressing global issues of our time, and understanding public perception and awareness of the topic is crucial for developing effective policies to mitigate its effects. While traditional survey methods have been used to gauge public opinion, advances in natural language processing (NLP) and data visualization techniques offer new opportunities to analyze user-generated content from social media and blog posts. In this study, a new dataset of climate change-related texts was collected from social media sources and various blogs. The dataset was analyzed using BERTopic and LDA to identify and visualize the most important topics related to climate change. The study also used sentence similarity to determine the similarities in the comments written and which topic categories they belonged to. The performance of different techniques for keyword extraction and text representation, including OpenAI, Maximal Marginal Relevance (MMR), and KeyBERT, was compared for topic modeling with BERTopic. It was seen that the best coherence score and topic diversity metric were obtained with OpenAI-based BERTopic. The results provide insights into the public's attitudes and perceptions towards climate change, which can inform policy development and contribute to efforts to reduce activities that cause climate change.

Reporting guidance for the One Health surveillance data collection

Abstract This technical report aims at guiding the data reporting to EFSA of analytical test results and the related metadata generated in the context of the One Health surveillance activities carried out by Member States under their direct grant agreements. The objective is to explain in detail the individual data elements that are included in the EFSA Standard Sample Description version 2 (SSD2) data model. The guidance is intended to support the reporting countries in data transmission using eXtensible Markup Language (XML) data file transfer through the Data Collection Framework (DCF) according to the protocol described in the EFSA Guidance on Data Exchange version 2 (GDE2). The data elements are explained, including information about data type, list of allowed terms and associated business rules. Instructions about how to report common sampling schemes are also provided to ensure harmonised reporting among countries.

Latest Computational Methods of Data Wrangling in Applied Linguistics

Data wrangling, the process of cleaning, transforming, and mapping raw data into a usable format, is a fundamental step in linguistic research. With the exponential growth of digital text and spoken language data, computational methods have become essential for managing and analyzing large datasets. This paper explores the latest computational techniques in data wrangling for linguistics, highlighting advancements in machine learning, natural language processing (NLP), and automated data cleaning technologies. These innovations enhance the efficiency and accuracy of data processing, enabling researchers to uncover patterns and generate insights previously unattainable. Additionally, the paper addresses the challenges inherent in data wrangling, including the integration of diverse data sources, the complexity of real-time data processing, and the importance of maintaining data quality and compliance with privacy regulations. Through a detailed examination of current methodologies and emerging trends, this research underscores the critical role of data wrangling in advancing linguistic studies and offers practical solutions for overcoming common obstacles in the field. The findings suggest that continued advancements in AI-driven tools and automated solutions will significantly impact the future of linguistic data analysis, making it more accessible and effective.

Verbal behavior and the future of social science.

Natural language processing (NLP)-previously the domain of a select few language and computer scientists-is undergoing an unprecedented surge in popularity across disciplines. The ubiquity of language data, alongside extremely rapid methodological innovations, has magnetized the field, attracting researchers with the promise of measuring, forecasting, and understanding the most central questions in business, psychology, biology, sociology, the humanities, and beyond. The power of language analysis to reveal insights into human thought, feeling, and behavior has become a core interest emerging from recent technological advances, which are being probed to unearth deeply embedded truths about the human condition. However, NLP research has reached a critical juncture, sitting at the cusp of societal transformation in many aspects of daily life. The details of how NLP research develops over the next 3-5 years will define this transformation. In this emerging, near-infinite space of NLP-driven research, we provide a critical frame of reference for how, when, and why these technologies should evolve in a particularly transdisciplinary manner. Specifically, we discuss (a) the urgency of pairing existing and emerging NLP research with existing scientific knowledge, theory, and principles from the behavioral sciences; (b) the coevolution of NLP technologies; and (c) the practical implications and ethical consequences of expanding language analysis using broader psychosocial theories of the human condition. While our discussion focuses principally on using language as a window in the individual mind, this topic holds substantial implications for other disciplines and lines of inquiry, including the dynamics of social interaction and beyond. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Management of race, ethnicity, and language data in the pediatric emergency department.

Management of race, ethnicity, and language data in the pediatric emergency department.

FRAGMENTS OF SLAVONIC-IRANIAN MYTHOPOETIC TEXT: SYNTACTIC CONSTRUCTIONS WITH *svęt- : *śṷanta-

The paper aims to reconstruct the common elements of the Slavonic and Iranian mythopoetic text, referring to the idea of the sanctity and sacred. The need to reconstruct the elements of this phenomenon of the communicative culture history makes out topicality of the proposed study. The objects of the investigation are: 1) set phrases, which in Iranian and Slavonic consist of the genetically identical lexemes; 2) Slavonic and Iranian set phrases, in which the only term is genetically common for both languages (*svętъ : *śṷanta-), while the second part here is represented by the synonyms with the different etymons; 3) two-termed (dithematic, bibasic) compound words with *svęt- : *śṷanta-, which are often the result of contraction of primary set phrases. Etymological, morphological and lexical-semantic features of analyzed units as well as reconstructed prototypes constitute the subject of the study. As the result of the proposed research, a set of ancient common to Iranians and Slavs formulaic expressions (and anthroponyms derived from them) are revealed; it is determined, that all the analyzed syntactic constructions were formed in the process of cultural contacts between speakers of both languages; their formal and semantic reconstruction is justified. Conclusions. Comparative-historical analysis of the ancient Slavonic and Iranian cliched phrases with the core component *svętъ : *śṷanta- and related to them dithematic personal names reveals the traces of the common Slavonic-Iranian text, formed within the poetic speech. Etymological composition of such «mycrotexts» along side with reflected therein complex of religious and ethic ideas, which is common to both cultural-linguistic traditions, leaves no doubt that we deal with the heritage of Slavonc-Iranian contacts. Especially indicative are the examples of full genetical identity in composition of formulaic constructions among the Slavs and Iranians. The semantic basis of formulaic expressions, common to both languages, is formed with the complex of discrete meanings, referring to the notions of abundance, life force and its increase. The material demonstrates the significant specific weight of Proto-Slavonic units with the exponent *svęt- alongside with the rudimentary and clearly areally and also chronologically limited data of Iranian languages. Permanent renewability of the tradition to use the syntactic cliches with *svęt- was facilitated by their involvement into the field of the derivation of Slavonic anthroponymic composites and later their demand in the texts of Christian religious topic. The perspectives for further work in the outlined vector are seen in comparison of the collected material with data of Baltic languages.

Providing a hybrid approach to increase the accuracy of intrusion detection systems in computer networks

Intrusion detection is a critical obstacle in the realm of security and data mining methodologies. Consequently, researchers have extensively investigated the quest for the swiftest and most precise means of identifying intrusions. Essentially, intrusion detection systems are tasked with recognizing any unauthorized activities, misuse, or harm inflicted upon a system, be it by internal or external users. Recently, in order to design intrusion detection systems, artificial intelligence and machine learning methods have been used, each of which has its own characteristics and advantages. Accordingly, this article focuses on using machine learning to improve the accuracy of the intrusion detection process. In fact, by using machine learning, trends, and patterns can be easily identified and thus used in a network environment to detect intrusion. It can be very useful. For this purpose, we utilize Radial Basis Function (RBF) neural networks and support vector machine (SVM) algorithm to improve decision-making and intrusion detection. By employing RBF neural networks, important features of the data are extracted, which in turnPlease check if the author details and affiliations are presented correctly. Kindly amend if necessary. enhance the overall performance of the solution and the efficiency of the SVM algorithm. This is because feature reduction ultimately leads to improved effectiveness of the SVM algorithm. In methods lacking this capability, the learning algorithm is compelled to utilize features that have no specific correlation with intrusion and essentially do not contribute to identifying attacks. Such learning approaches essentially learn from noisy data, which negatively impacts the intrusion detection solution. Finally, the proposed solution was evaluated using Python programming language and KDD99 data set. The results of the evaluation indicate that the proposed solution has a higher accuracy and precision than other evaluated solutions. So, the accuracy is 97, and the precision is over 99%.

SOME OBSERVATIONS ON THE LANGUAGE CHARACTERISTICS OF A UKRAINIAN DIALECT’ SPEAKER OF A NON-EXISTING VILLAGE OF ODESA REGION

The purpose of the article is to describe the speech characteristics of the informant, a native resident of the village of Olenokorytske, Krasnooknyanskyi (Podilskyi) district, Odesa region. The object of the research is the speech of the informant, a former resident of this village; subject — dialect elements in her speech. Both general scientific methods and purely linguistic methods are used based on linguopersonological approach: descriptive and comparative methods, the method of direct interview of the informant, the method of observation (included and excluded), component analysis, the method of documenting and transcribing language data, in particular, the method of practical transcription. The scientific novelty of the study is determined by the fact, that for the first time in domestic linguistics, attention was drawn to the dialect of the village of Olenokorytske, Krasnooknyanskyi (Podilskyi) district, Odesa region of Ukraine. The dialectal elements are identified and analyzed at the phonetic, morphological, syntactic, lexical, phraseological and paremiological levels. The facts of interdialect influence are established. The obtained results of the study allow us to come to conclusion that the informant’s speech is characterized by the stability of dialectal elements at all levels of the linguistic system, preserving certain archaic forms, regional lexical and phraseological inernationalisms. The practical value lies in the possibility of using its results in research in the field of dialectology, history of linguistics, cognitive linguistics, sociolinguistics, linguopersonology. The prospects of the research can be seen in the further detailed description of the language picture of the informant’s world.

A vision-language-guided robotic action planning approach for ambiguity mitigation in human–robot collaborative manufacturing

Human–robot collaboration (HRC) has been recognized as a potent pathway towards mass personalization in the manufacturing sector, by leveraging the synergy of human creativity and robotic precision. Previous approaches rely heavily on visual perception to autonomously comprehend the HRC environment. However, the inherent ambiguity in human–robot communication cannot be consistently neutralized by relying solely on visual cues. With the recently soaring popularity of large language models (LLMs), the consideration of language data as a complementary information source has increasingly drawn research attention, while the application of such large models, particularly within the context of HRC in manufacturing, remains largely under-explored. In response to this gap, a vision-language reasoning approach is proposed to mitigate the communication ambiguity prevalent in human–robot collaborative manufacturing scenarios. A referred object retrieval model is first designed to alleviate the object–reference ambiguity in the human language command. This model is then seamlessly integrated into an LLM-based robotic action planner to achieve an improved HRC performance. The effectiveness of the proposed approach is demonstrated empirically through a series of experiments conducted on the object retrieval model and its application in a human–robot collaborative assembly case.

Contribution of Artificial Intelligence to Learning the Arabic Language

The knowledge revolution in the middle of the twentieth century brought about a digital transformation in various aspects of human development, and in view of the knowledge data in machine language, it became necessary to computerize the Arabic language as a means of transferring knowledge, and to make it compatible with modern means of communication in artificial intelligence algorithms to help humans overcome the difficulties of communication and learning. Therefore, researchers in the field of artificial intelligence sought to understand human perception and try to simulate it through the development of computer systems that deal with different levels of languages. Researching a descriptive and analytical reading of the reality of the Arabic language in its relationship to artificial intelligence and computing, and the extent to which it benefits from technological and informational progress, as well as possible horizons that will contribute to solving its problems and challenges.