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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.

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.

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.

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.

Natural Language Processing (NLP) technologies are being increasingly incorporated into business and pervasive systems to better user engagement, minimize input friction, and make applications more engaging and easy to talk with. This research provides a results-driven framework for adapting NLP into Oracle APEX—Oracle's low-code development platform—APEX aimed at improving the interface and responsiveness of web-based systems used in different contexts. The architecture proposed enables natural language query processing, automatic form creation, and contextual help by embedding workflows in APEX with lightweight NLP pipelines for intent detection, entity recognition, and adaptive dialogues. The experiments were along the lines of task automation, data retrieval, and user profile management among other domains. The findings show measurable enhancements in the completion time of tasks, satisfaction levels of users, flexibility in input, alongside a drastic reduction in errors during interactions. Latency measurement, system throughput, and other user interface metrics confirm the integration approach’s scalability and realistic performance bound with operational user loads. Within the APEX framework, the study also outlines methods for capturing user input design, collecting feedback, and updating the NLP models iteratively devising fall-back strategies during haptic interactions. This research presents a paradigm that is easy to scale, maintain, and manage as well as responsive towards versatile user needs through conversational intelligence in ubiquitous systems using Oracle APEX.

With the continuous development of new media technology, the spiritual needs of the masses have been greatly satisfied and the aesthetic ability has also been significantly improved compared with the past. From the current point of view, “literary works,” as the spiritual food of contemporary people, are promoting social spirit. The use of natural language processing and knowledge graph technology can improve cultural cognition to promote the dissemination and development of classic English literature, which has become a necessary means of dissemination of classic English literature. Most of the existing classic English literary works are appreciated based on modern literature datasets. Nowadays, with the continuous development of new media technology, there are fewer studies on the dissemination and cultural cognition of classic English literary works. This makes it impossible for readers to obtain cultural cognition from classic English literary works, making it difficult for the dissemination and development of classic English literary works. In view of the above problems, using natural language processing and knowledge graph technology, taking Shakespeare's play “Hamlet” represented by classic English literary works as an example, the research on the construction method of knowledge graph is carried out and the cultural characteristics in literary works are extracted and analyzed. In parsing, a bidirectional gated recurrent unit network model based on hybrid character embedding is proposed. Based on n-gram embedding, by combining pretraining embedding and radical embedding, it can fully consider the rich semantic information in English literature works to extract. Feature: in terms of named entity recognition, based on the existing iterative atrous convolutional network model, an iterative atrous convolutional network model is proposed. To get the best sequence label and get the last labeled entity information, in terms of knowledge graph construction and visual query, a workflow method for building knowledge graph from unstructured text is proposed and a flask-based knowledge graph visual query system is designed, which applies the best model of the above two tasks. We decode the complete “Hamlet” text, extract entities and their semantic links as nodes and relationships in the knowledge graph, store knowledge through the graph database, and finally form a visual query system that combines the front and back end.

BackgroundModernizing medical education by using artificial intelligence and other new technologies to improve the clinical thinking ability of medical students is an important research topic in recent years. Prominent medical universities are actively conducting research and exploration in this area. In particular, given the shortage of human resources, the need to maintain social distancing to prevent the spread of the epidemics, and the increase in the cost of medical education, it is critical to harness online learning to promote medical education. A virtual case learning system that uses natural language processing technology to process and present a hospital’s real medical records and evaluate student responses can effectively improve medical students’ clinical thinking abilities.ObjectiveThe purpose of this study is to develop a virtual case system, AIteach, based on actual complete hospital medical records and natural language processing technology, and achieve clinical thinking ability improvement through a contactless, self-service, trial-and-error system application.MethodsCase extraction is performed on a hospital’s case data center and the best-matching cases are produced through natural language processing, word segmentation, synonym conversion, and sorting. A standard clinical questioning data module, virtual case data module, and student learning difficulty module are established to achieve simulation. Students can view the objective examination and inspection data of actual cases, including details of the consultation and physical examination, and automatically provide their learning response via a multi-dimensional evaluation system. In order to assess the changes in students’ clinical thinking after using AIteach, 15 medical graduate students were subjected to two simulation tests before and after learning through the virtual case system. The tests, which included the full-process case examination of cases having the same difficulty level, examined core clinical thinking test points such as consultation, physical examination, and disposal, and generated multi-dimensional evaluation indicators (rigor, logic, system, agility, and knowledge expansion). Thus, a complete and credible evaluation system is developed.ResultsThe AIteach system used an internal and external double-cycle learning model. Students collect case information through online inquiries, physical examinations, and other means, analyze the information for feedback verification, and generate their detailed multi-dimensional clinical thinking after learning. The feedback report can be evaluated and its knowledge gaps analyzed. Such learning based on real cases is in line with traditional methods of disease diagnosis and treatment, and addresses the practical difficulties in reflecting actual disease progression while keeping pace with recent research. Test results regarding short-term learning showed that the average score (P < 0.01) increased from 69.87 to 85.6, the five indicators of clinical thinking evaluation improved, and there was obvious logical improvement, reaching 47%.ConclusionBy combining real cases and natural language processing technology, AIteach can provide medical students (including undergraduates and postgraduates) with an online learning tool for clinical thinking training. Virtual case learning helps students to cultivate clinical thinking abilities even in the absence of clinical tutor, such as during pandemics or natural disasters.

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.

Advances in natural language processing (NLP) and automatic speech recognition and processing technologies offer new opportunities for language testing. Despite their potential uses on a range of language test item types, relatively little work has been done in this area, and it is therefore not well understood by test developers, researchers or users in language assessment. This paper introduces NLP for language assessment as an area of inquiry and practice by describing the historical roots coming from computational linguistics, statistical NLP, speech recognition and processing technologies, language assessment, and computer-assisted language learning. It outlines uses of NLP and speech recognition and processing technologies in language assessment through illustrations of current testing projects, and identifies areas in need of further development.

The purpose of the work is to analyze models, natural language processing methods, and select modern technologies for training these models, as well as to develop a web service for creating tests based on text analysis using natural language processing technologies. The study considers methods and algorithms for intelligent data analysis to generate questions and correct and incorrect answers from the text. The authors justify the choice of a neural network for generating tests based on English and Ukrainian text, and characterize data sources for training. The study also describes the activity of the proposed model, which will serve as a basis for creating a web service. After a detailed review of these datasets, the necessary data for the experiment were extracted and transformed into a convenient format for use. The training algorithm for 6 models was designed and implemented, and valuable metrics were obtained after their training. Additionally, a server-side and web interface were developed to interact with each other.

An automatic literature knowledge graph and reasoning network modeling framework based on ontology and natural language processing

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.