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

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.

447 Background: Mortality from bladder cancer (BC) increases exponentially once it invades the muscle. At the population level, accurate delineation of these patients is challenging. Methods: To develop and validate a natural language processing (NLP) model for automatically identifying muscle-invasive BC (MIBC) patients, aiding in population-based BC research. All patients with a CPT code for transurethral resection of bladder tumor (TURBT) (N = 76,060) were selected from the Department of Veterans Affairs (VA) Corporate Data Warehouse database. A sample of 600 patients (with 2,337 full-text notes) who had TURBT and confirmed pathology results were selected for NLP model development (500 patients) and validation (100 patients). Muscle-invasion (yes/no), unknown, or no cancer, were confirmed by detailed chart review of pathology notes. The NLP performance was assessed by calculating the sensitivity, positive predictive value (PPV), and overall accuracy at the individual note and patient levels. Results: In the validation cohort, the NLP model had overall accuracy of 88% and 92% at the note and patient levels. Specifically, PPV and specificity for predicting muscle-invasion on note level were 83% and 70%, respectively. The model classified non-muscle invasive BC (NMIBC) with 98% sensitivity at both the note and patient levels. Although the sensitivity for MIBC was 70% for note-level determination, the sensitivity was 86% when evaluated at the patient level. When applying the model to 71,200 patients VA-wide, the model classified 13,642 (19%) as having MIBC and 47,595 (66%) as NMIBC. The NLP model was able to identify invasion status for 96% TURBT patients at the population level. Inherent limitations include relatively small training set given the size of the VA population. Conclusions: This NLP model for identifying muscle-invasion at the population level had high accuracy. The NLP model may be a practical and accurate tool for efficiently identifying BC invasion status and may potentially aid in population-based BC research in the VA.

Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation

To develop and evaluate an internally validated natural language processing (NLP) model to determine guideline adherence of antibiotic decisions in the emergency department (ED) for skin and soft tissue infections (SSTIs). This cross-sectional pilot study developed and applied a random forest (RF)/NLP model to classify clinical narratives of patients with skin infections as requiring either methicillin-resistant Staphylococcus aureus (MRSA) or non-MRSA antibiotic coverage. Conducted at a quaternary care children's hospital with an annual volume of 50,000 ED visits, our study included patients aged 1 to 18 years presenting for SSTIs to the ED from July 1, 2018, through June 30, 2023. Main outcomes included the NLP model's sensitivity, specificity, and receiver operator characteristic (ROC) curve compared to gold standard manual physician review of the medical records. A total of 1588 patients were part of the training data set, with an additional 423 patients utilized for validation. The RF model achieved an area under the curve (AUC) of 0.99, 97.0% sensitivity, and 94.9% specificity. In the validation data set, the model achieved 96.6% sensitivity and 90.1% specificity. Performance remained strong despite absent/missing patient history in certain narratives. The NLP model demonstrated that automated analysis of clinical narratives for determining guideline adherence is feasible. Despite missing data in narratives, the model's high performance suggests potential for broader application. Given the rise in antibiotic-resistant infections and the role of judicious antibiotic use, developing automated systems through NLP can significantly contribute to health care delivery and patient safety. The methodology of this study provides a feasible, sustainable path for similar applications in the emergency setting.

Understanding treatment patterns and effectiveness for patients with metastatic prostate cancer (mPCa) is dependent on accurate assessment of metastatic status. The objective was to develop a natural language processing (NLP) model for identifying patients with mPCa and evaluate the model's performance against chart-reviewed data and an International Classification of Diseases (ICD) 9/10 code-based method. In total, 139,057 radiology reports on 6,211 unique patients from the Department of Veterans Affairs were used. The gold standard was metastases by detailed chart review of radiology reports. NLP performance was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and date of metastases detection. Receiver operating characteristic curves was used to assess model performance. When compared with chart review, the NLP model had high sensitivity and specificity (85% and 96%, respectively). The NLP model was able to predict patient-level metastasis status with a sensitivity of 91% and specificity of 81%, whereas sensitivity and specificity using ICD9/10 billing codes were 73% and 86%, respectively. For the NLP model, date of metastases detection was exactly concordant and within < 1 week in 55% and 58% of patients, compared with 8% and 17%, respectively, using the ICD9/10 billing codes method. The area under the curve for the NLP model was 0.911. A limitation is the NLP model was developed on the basis of a subset of patients with mPCa and may not be generalizable to all patients with mPCa. This population-level NLP model for identifying patients with mPCa was more accurate than using ICD9/10 billing codes when compared with chart-reviewed data. Upon further validation, this model may allow for efficient population-level identification of patients with mPCa.

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.

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.

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.

Medical educators pay increasing attention to the potential utility of narrative data for assessment, but lack of efficient and standardized ways of interpreting the data have limited its use. Natural language processing (NLP) algorithms could provide new insights for using narrative data within assessment especially identifying at-risk or low-performing students. Assessment data were reviewed from 16 cohorts of medical students from the University of Cincinnati College of Medicine (graduating classes of 2006-2022). A T-score Average (TSA) was calculated for each student based on clerkship assessment data. Narrative data from core clerkship evaluations in responses to the prompt "any opportunities for improvement" were utilized for analysis. The narrative data and calculated TSA were then used to train and test 4 NLP models with the goal of utilizing NLP to identify at-risk students as defined by the bottom 10% average TSA score. Based on typical NLP methods, the developed NLP models all performed adequately in identifying student performance with an overall accuracy of 0.8 across all 4 models. However, none of the NLP models was able to identify students within the bottom 10% of performance. During this process, we uncovered the presence of "copy/paste" behavior, a previously undocumented phenomenon within narrative data where preceptors duplicated comments from 1 student to the next. Training NLP models including "copy/paste" comments improved NLP ability to identify students within the bottom and top 10% of performance. NLP models were unable to accurately identify at-risk students. Model accuracy increased with the inclusion of "copy/paste" comments, indicating there may be some discriminatory functionality within aspects of narrative data beyond keywords such as lexical diversity and word quantity. Future work will explore how these other aspects of narrative data associate with performance and utilize more advanced large language models of analysis.Based on typical natural language processing (NLP) methods, the tested NLP models performed adequately in identifying student performance with an overall accuracy of 0.8 across all models. However, none of the NLP models was able to identify students within the bottom 10% of performance, revealing widespread "copy/paste" behavior in evaluations that may limit narrative data's discriminatory utility.

This research paper describes an experimental study to determine the effectiveness of using Chat GPT based natural language processing (NLP) model to improve the writing proficiency of ESL students. The study shows how such approach can be integrated into the teaching process to improve the quality of writing skills. The paper provides an overview of methods of teaching writing according to certain criteria for assessing writing skills. The effectiveness of the model is assessed in comparison with traditional teaching methods. Recommendations are given for optimizing the learning process using NLP technologies. The conclusion highlights the significance of the technology used in improving writing skills and discusses the prospects for using the Chat GPT model to optimize these skills in ESL students.

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...

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.

108 Background: Classification of disease response is an essential task in cancer research and needs to be done at scale. Automating this process can improve efficiency in the generation of real-world evidence, potentially leading to better patient outcomes. We aim to develop and evaluate Natural Language Processing (NLP) models for this task. Methods: Using 6203 computed tomography (CT) and 1358 magnetic resonance imaging (MRI) reports from 587 patients with lung cancer of all stages seen at the National Cancer Centre Singapore (NCCS), we trained four NLP models (BioBERT, RadBERT-RoBERTA, BioClinicalBERT, GatorTron) to classify the reports into one of four categories: no evidence of disease, stable disease, partial response or disease progression. Model output was compared against human-curated ground truth and performance was evaluated by accuracy. Results: Of the 4 models, GatorTron performed the best (accuracy = 97.1%), followed by RadBERT-RoBERTA (accuracy = 96.2%), BioBERT (accuracy = 94.2%), with BioClinicalBERT being last (accuracy = 90.4%). NLP Model runtimes for the dataset were relatively short, with BioBERT and BioClinicalBERT taking 3 minutes per epoch, RadBERT-RoBERTA taking 6 minutes per epoch, and GatorTron taking 10 minutes per epoch on a single central processing unit (CPU). Conclusions: We have demonstrated the effectiveness of NLP models for classifying disease responses in radiology reports of lung cancer patients. This has the potential to help derive progression-free survival for real-world evidence generation.