Integration of NLP (Natural Language Processing)-Based Technology in Japanese Language Learning

Natural language processing (NLP) technology is an important constituent of artificial intelligence, focusing on the interaction between computers and human natural language, with the aim of enabling computers to understand, analyze, generate and process human languages. The fields of legal aid and judicial practice are also strongly related to texts, therefore, natural language processing technology has shown great potential in the legal field nowadays. In this study, the author reviews the development status, core technologies, application areas, and application value of natural language processing technology in the fields of legal aid and judicial practice. In addition, the study summarizes the difficulties and challenges faced by natural language processing technology in the field of legal aid and judicial practice. In particular, the current legal natural language processing technology still has technical problems, such as difficulty in processing complex and long legal texts, a lack of certain logical reasoning ability, insufficient understanding of non-professional questions, and a lack of public datasets, which leads to high model training costs, etc. In response to these problems, the study points out the need to make natural language processing models more adaptable to legal language, and proposes suggestions such as enhancing the popularity of models and open datasets.

Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data. We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included. We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F1 score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care. Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.

Educational systems in Japanese language learning face unique challenges in efficiently allocating resources while providing personalized learning experiences across both online and offline environments. This study presents a novel approach combining deep learning techniques with adaptive resource management to optimize Japanese language educational resource allocation, specifically addressing challenges in kanji recognition, pronunciation patterns, and grammar acquisition. Our framework implements a hybrid architecture that integrates behavioral pattern recognition with time-weighted correlation analysis, particularly focusing on Japanese language learning behaviors such as character writing sequences, listening comprehension patterns, and speaking practice engagement. The system was evaluated using a comprehensive dataset comprising 2.5 million student interaction records collected over three academic semesters from Japanese language courses across multiple proficiency levels. Experimental results demonstrate significant improvements across multiple performance metrics, with the model achieving 92% accuracy in Japanese learning behavioral pattern recognition and a 27% reduction in infrastructure costs. The implementation of adaptive learning paths resulted in a 41% improvement in kanji retention rates and a 52% enhancement in oral communication skills among struggling students. The system’s effectiveness was validated through rigorous testing under varying learning scenarios, showing robust performance in both online and offline Japanese language learning environments. The proposed framework demonstrates superior scalability and adaptability compared to traditional approaches, particularly in handling diverse aspects of Japanese language acquisition. These findings suggest that our approach offers a practical solution for Japanese language educational institutions seeking to enhance learning outcomes while optimizing resource utilization across blended learning environments.

Using natural language processing (NLP) technology to analyze and organize textual information in psychiatric electronic medical records can identify undiscovered factors associated with treatment discontinuation. This study aimed to evaluate brexpiprazole treatment continuation rate and factors affecting brexpiprazole discontinuation using a database that employs the MENTAT® system with NLP technology. This retrospective observational study evaluated patients with schizophrenia who were newly initiated on brexpiprazole (April 18, 2018-May 15, 2020). The first prescriptions of brexpiprazole were followed up for 180days. Factors associated with brexpiprazole discontinuation were assessed using structured and unstructured patient data (April 18, 2017-December 31, 2020). The analysis population comprised 515 patients; mean (standard deviation) age of patients was 48.0 (15.3) years, and 47.8% were male. Using Kaplan-Meier analysis, the cumulative brexpiprazole continuation rate at 180days was 29% (estimate: 0.29; 95% confidence interval, 0.25-0.33). Univariate Cox proportional hazards analysis identified 16 variables independently associated with brexpiprazole discontinuation. Multivariate analysis identified eight variables associated with treatment discontinuation: variables with hazard ratio <1 were the presence of physical complications, longer hospitalization duration, and maximum chlorpromazine-equivalent dose of antipsychotics of >200 to ≤400mg/day vs ≤200mg/day in the past year; variables with hazard ratio >1 were previous electroconvulsive therapy, availability of key contact person information, a history of crime committed/reported, increase in brexpiprazole dose to 2mg in >28days, and appearance/worsening of symptoms other than positive symptoms. In conclusion, we identified potential new factors that may be associated with brexpiprazole discontinuation, which may improve the treatment strategy and continuation rate in patients with schizophrenia.

Typically, searching for information in a document collection amounts to refining a query and then scanning a large number of documents to determine their relevance. Active Summarization Having Related Active Markup (ASHRAM) is a facility for representing and automatically selecting, marking, and linking useful and/or salient items in a document, to make it easier for the user to determine the main points in a document or navigate through documents without having to read all of them. ASHRAM is a novel client server system and user interface consisting of dynamically generated HTML, JavaScript and Java which requests information from a document database stored on a server. We describe a system for summarization by sentence extraction and a user interface for representation that allows the user to exploit the summary not only as an aid for relevance assessment of documents, but as an active aid to document navigation. The server-based scalable text summarization and keyword extraction system uses Natural Language Processing (NLP) technology and corpus-based NLP techniques in the foreground and databases constructed using NLP technology in the background.

414 LANGUAGE, VOLUME 67, NUMBER 2 (1991) Brixhe & Lejeune's 1984 Corpus des inscriptions paléo-phrygiennes. Since Phrygian is both closely related to Greek and preserved in part through Greek testimony, N has chosen to present his account as a history of PhrygianGreek linguistic contact. This procedure may explain his vagueness in discussing shared features of the two languages: although he calls them 'common innovations' (10), he seems elsewhere to regard them as the result of borrowing during a hypothesized period of close contact among Greek, Phrygian, and several other IE languages in the south Balkans (5-6). Some of these shared features are quite striking—e.g. factitives in *-oyô like Gk. orthöö 'straighten'. Phr. kakoioi (11, 13), and laryngeal vocalization followed by umlaut in Gk. ónoma, Phr. onoman = [b] not [bh], this development is trivial and proves only that Phrygian did not share the Greek change to [ph] etc.). Pp. 6-10: Common retentions of Greek and Phrygian (e.g. the augment and the thirdperson present middle ending -toi). Pp. 10-13: Common innovations of Greek and Phrygian (e.g. agent nouns in -tä, Gk. autos 'self = Phr. autos 'particular'). Pp. 13-16: Inherited lexical material of Phrygian which is lost or only marginally preserved in Greek. Pp. 16-18: Sometime after the hypothesized period of south Balkan close contact with Greek, the Phrygians migrated to Anatolia, where they are attested in historical times. Contact with speakers ofAnatolian languages led to borrowings into Phrygian (mainly names, e.g. Midas). Pp. 18-19: Greek expansion in the first millennium led to contact and hence Greek loans into Phrygian. Pp. 19-22: Direct attestation of Phrygian lost from the third century B.C.E. through the first century CE. Pp. 22-23: Neo-Phrygian attested in grave inscriptions, still in part uninterpretable . Although Phrygian is unlikely ever to play a significant role in IE historical linguistics, it does, as N remarks in conclusion (23), repay time spent applying to it our knowledge of IE. N's essay is a valuable contribution to the project of understanding Phrygian. [Andrew Garrett, University of Texas, Austin.] Natural language processing technologies in Artificial Intelligence: The science and industry perspective. By Klaus K. Obermeier. Chichester : Ellis Horwood, 1989. Pp. 263. Natural language processing (NLP) is the area of research concerned with the many aspects of 'natural' language communication with computers . In this book Obermeier provides an introduction to NLP from both commercial and academic perspectives, primarily aimed at businessmen and data-processing professionals. His presentation is nontechnical, though very comprehensive . Linguists will find the book an accessible introduction to the practical issues in NLP, which may help to clarify the direction and emphasis in this area of language research. The book's organization is excellent, making it very easy to read, although the typographic and editorial quality are not the best. The twelve chapters are divided into three sections: 'NLP in perspective' (17-92). which provides an introduction to the structure of NLP systems and the approaches and formalisms used to implement them; 'Applications' (93-185), in which six major applications of NLP are described: and 'Bottom line' (187-232), where O presents his own evaluation of the state of NLP research and current systems. A good list of references (239-59) is also included. Much of the material in the first section will be familiar to linguists, since the design of NLP systems is largely based on formal linguistic analyses in the areas ofphonology, morphology, syntax, and semantics. In addition, methods for knowledge representation and reasoning drawn from Artificial Intelligence are explained in relation to their relevance to NLP development. This nontechnical overview of the techniques used in NLP is up-to-date. It includes a discussion of parsing methods ranging from contextfree parsing, unification-based parsing, and ATNs to such knowledge-representation schemes from AI as production rules, semantic networks, and frames, to the new connectionist models. BOOK NOTICES 415 The second section comprises the largest part of the book, with one chapter devoted to each of the applications addressed: natural-language interfaces, machine translation, text processing/ understanding, text generation, speech processing , and writing aids. For each application there is a brief...

This study investigates the application of Natural Language Processing (NLP) technologies in enhancing creative writing among poets, focusing on how these advanced computational tools can support and transform the poetic composition process. As the digital landscape evolves, the integration of artificial intelligence into the arts presents both opportunities and challenges, prompting a critical examination of the role of NLP in fostering creativity and artistic expression. The research is grounded in a comprehensive review of existing literature surrounding NLP applications in creative writing, highlighting the historical evolution of these technologies and their implications for literary practices. The study employs a mixed-methods approach, combining qualitative and quantitative methodologies to explore the experiences of poets utilizing NLP tools. Through in-depth interviews and real-time interaction sessions, the research captures the nuanced dynamics between human creativity and machine-generated insights, providing a rich understanding of how poets engage with these systems. Key findings reveal that NLP technologies significantly enhance the creative writing process by offering immediate feedback, generating contextual suggestions, and alleviating instances of writer's block. Participants reported a collaborative relationship with the technology, viewing NLP tools as creative partners rather than mere aids. This partnership not only enriches the compositional experience but also encourages poets to experiment with language, form, and thematic exploration, thus broadening their artistic horizons. Moreover, the study addresses the ethical considerations surrounding the use of NLP in poetry, particularly in relation to authorship, authenticity, and the risk of homogenization in poetic voice. As poets navigate the complexities of integrating technology into their craft, the need for clear guidelines regarding intellectual property rights and the preservation of diverse poetic expressions becomes increasingly pertinent. This research contributes to the growing discourse on the intersection of technology and the arts, advocating for a nuanced understanding of how NLP can be harnessed to enhance, rather than replace, human creativity. By illuminating the transformative potential of NLP in the realm of creative writing, this study offers valuable insights for poets, educators, and technologists alike, suggesting pathways for future exploration and collaboration in the evolving landscape of literary expression.

A sentence-level text adversarial attack algorithm against IIoT based smart grid

Natural Language Processing (NLP) and related technologies are critical for the success of many Internet applications such as digital libraries, e-commerce and customer service. This talk presents some recent research efforts and trends of four sets of NLP technologies for Internet applications. First, neural language model has been a very popular research direction in the last a few years that serves as the foundation of many NLP technologies and has significantly improved the performance of many applications; Second, machine translation techniques have been substantially advanced to better bridge the language barriers for many Internet applications; Third, the identification of inappropriate Internet text information (e.g., pornographic content) is a challenging research topic due to the diversified text representation; Fourth, machine reading comprehension has been become an important question and answering technology to directly satisfy information needs of many Internet users. These technologies will be discussed with examples from large-scale real-world applications.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

The development of machine translation technology closely follows that of information science. It is a breakthrough in natural language processing and a big step forward in the field of artificial intelligence. The purpose of this paper is to study the application of machine translation technology based on natural language processing. This paper introduces the relationship between natural language processing and machine translation technology, expounds the research importance of machine translation technology in natural language processing and the research status at home and abroad. The application of neural network in natural language processing machine translation system is introduced. At the same time, for the existing machine translation system, a neural network machine translation system based on reinforcement learning is proposed. And analyze the machine translation of different models through case studies. Finally, the Python software is used to implement the program, and the accuracy of the Transformer-PG-NMT model is obtained.

We have used data on patents and publications, and from an Internet-based survey, to analyse corporate technological activities in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) technologies. Two distinct clusters of firms exist: large firms mainly in telecommunications, desktop computing and consumer electronics; and small firms specialising in speech technologies. The small specialised firms depend heavily on nearby universities and public research institutes and, to some extent, on nearby large firms; their relations with the large firms are complementary as well as competitive. Similar patterns can be observed in other, recently emerging, "new science"-based technologies. Integration between ASR and NLP has so far been weak with the two research communities functioning more or less independently, with the former progressing more rapidly than the latter. Having built technological capabilities in ASR and NLP with a small proportion of their corporate technological resources, the large firms have two options depending on the rate of progress in these technologies (especially NLP) in the future. If it is high, substantial investments (including those in complementary technologies) could open up massive market opportunities. If it is low, modest investments will allow the exploitation of niche markets.

This proposal outlines a plan for bridging the gap between technology experts and society in the domain of Artificial Intelligence (AI). The proposal focuses primarily on Natural Language Processing (NLP) technology, which is a major part of AI and offers the advantage of addressing problems that non-experts can understand. More precisely, the goal is to advance knowledge at the same time as opening new communication channels between experts and society, in a way which promotes non-expert participation in the conception of NLP technology. Such interactions can happen in the context of open-source development of languages resources, i.e. software tools and datasets; existing usages in various communities show how projects which are open to everyone can greatly benefit from the free participation of enthusiastic contributors (participation is not at all limited to software development). Because NLP research is mostly experimental and relies heavily on software tools and language datasets, this project proposes to interconnect the societal issues related to AI with the NLP research resources issue.

To review studies of Natural Language Processing (NLP) systems that assist in cancer care, explore use cases and summarise current research progress. A scoping review, searching six databases (1) MEDLINE, (2) Embase, (3) IEEE Xplore, (4) ACM Digital Library, (5) Web of Science, and (6) ACL Anthology. Studies were included that reported NLP systems that had been used to improve cancer management by patients or clinicians. Studies were synthesised descriptively and using content analysis. Twenty-nine studies were included. Studies mainly applied NLP in mixed cancer types (n=10, 34.48%) and breast cancer (n=8, 27.59%). NLP was used in four main ways: (1) to support patient education and self-management; (2) to improve efficiency in clinical care by summarising, extracting, and categorising data, and supporting record-keeping; (3) to support prevention and early detection of patient problems or cancer recurrence; and (4) to improve cancer treatment by supporting clinicians to make evidence-based treatment decisions. Studies highlighted a wide variety of use cases for NLP technologies in cancer care. However, few technologies have been evaluated within clinical settings, none have been evaluated against clinical outcomes, and none have been implemented into clinical care. NLP has the potential to improve cancer care via several mechanisms, including information extraction and classification, which could enable automation and personalization of care processes. Additionally, NLP tools such as chatbots show promise in improving patient communication and support. However, there are deficiencies in the evaluation and clinical integration challenges. Interdisciplinary collaboration between computer scientists and clinicians will be essential if NLP technologies are to fulfil their potential to improve patient experience and outcomes. Registered Protocol: https://doi.org/10.17605/OSF.IO/G9DSR.

Natural Language Processing (NLP) has rapidly evolved into a core enabler of enterprise digital transformation, empowering organizations to automate workflows, extract insights from unstructured text, and enhance data-driven decisionmaking processes. This paper presents a comprehensive survey of current NLP technologies, emphasizing their practical integration within enterprise environments. We review the development of large-scale pre-trained models such as BERT, GPT-3, and domain-specific variants, highlighting their capabilities in applications including sentiment analysis, intelligent document processing, regulatory compliance monitoring, customer service automation, and risk assessment across sectors such as healthcare, finance, and retail. Key deployment considerations such as cloud infrastructure, container orchestration, data pipelines, and governance strategies are critically analyzed to guide scalable and secure implementation. Furthermore, we synthesize existing costaware ROI evaluation approaches to assess the financial viability and business value of NLP deployments, integrating performance metrics, operational costs, and strategic benefits. Real-world industrial case studies are discussed, demonstrating best practices and challenges in deploying NLP systems at scale, including issues of model drift, explainability, data privacy compliance, and integration with enterprise IT ecosystems. Additionally, we explore emerging trends such as multilingual adaptation, voiceenabled NLP, and environmentally sustainable AI deployment strategies. This survey aims to provide researchers, practitioners, and decision-makers with a structured understanding of how NLP technologies can be effectively leveraged to deliver measurable business outcomes, enhance organizational efficiency, and drive strategic innovation in real-world enterprise settings. This paper reviews existing ROI evaluation approaches and deployment strategies for NLP in enterprises.