Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

Aspect-Based Sentiment Analysis(ABSA)is a fundamental area of research in Natural Language Processing(NLP).Within ABSA,Aspect Sentiment Quad Prediction(ASQP)aims to accurately identify sentiment quadruplets in target sentences,including aspect terms,aspect categories,corresponding opinion terms,and sentiment polarity.However,most existing research has focused on English datasets.Consequently,while ASQP has seen significant progress in English,the Chinese ASQP task has remained relatively stagnant.Drawing inspiration from methods applied to English ASQP,we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task,ultimately improving ASQP performance in the Chinese context.Ultimately,under the same pre-training model configuration,our approach achieved a 5.79%improvement in the F1 score compared to the previously leading method.Furthermore,when utilizing a larger model with reduced training parameters,the F1 score demonstrated an 8.14%enhancement.Additionally,we suggest a novel evaluation metric based on the characteristics of generative models,better-reflecting model generalization.Experimental results validate the effectiveness of our approach.

Gastric cancer immunotherapy:A scientometric and clinical trial review

This letter is intended to arouse your interest in a recent review of comprehensive scientometrics and clinical trials on immunotherapy for gastric cancer(GC).Our study reviews recent advances in immunotherapy in the field of GC and highlights its new prospects as a treatment for GC.Our research reveals China’s leadership in this field,as well as new therapeutic strategies such as immune checkpoint inhibitors,cellular immunotherapy,and vaccines.The combined findings highlight the potential of immunotherapy to improve survival and quality of life in patients with stomach cancer.We believe that this study will provide important guidance for the future direction of the GC treatment field.

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Reviewer Acknowledgements

The editors of International Journal of Ophthalmology gratefully acknowledge the members of IJO Editorial Board and reviewers from 57 countries and regions who participated in the peer-reviews and provided their valuable comments between Nov.1^(st),2022 and Oct.31^(st),2023.

Multidisciplinary approaches in the management of advanced hepatocellular carcinoma:Exploring future directions

Recently,we read the article“Pathologically successful conversion hepatectomy for advanced giant hepatocellular carcinoma after multidisciplinary therapy:A case report and review of the literature”published in the World Journal of Gastrointestinal Oncology.The prognosis of advanced hepatocellular carcinoma(HCC)is poor,and multidisciplinary comprehensive treatment is currently the main research direction.This case report demonstrated the efficacy of the combination therapy of transcatheter arterial chemoembolization,hepatic arterial infusion chemotherapy,epclusa,lenvatinib and sintilimab for a patient with advanced HCC,and the report can serve as a reference for clinical practice.We would also like to share some of our views.

Additional comments on foot reflexology treatment for sensorineural hearing loss in infant

Currently,treatment options for infant sensorineural hearing loss(SNHL)are limited.This article describes a novel case of SNHL in an infant successfully treated with foot reflexology,along with observed brain activity changes before and after treatment,as indicated by functional magnetic resonance imaging.Hence,this commentary discusses the case and our viewpoints regarding foot reflexology for treating SNHL.

Emotional differences based on comments on doctor-patient disputes with varying levels of severity

BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.

Enhancing User Security on Instagram: A Multifaceted AI System for Filtering Abusive Comments

Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.

The Impact of Classroom Evaluation on Chinese Learning Motivation:A Comparative Study of Oral Feedback and Written Comments

Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language learning,oral feedback and written comments are two prevalent evaluation methods.This paper explores how these different types of feedback impact students’motivation,learning outcomes,and participation.By comparing the immediacy of oral feedback with the systematic nature of written comments,this study seeks to provide insights into how educators can utilize classroom evaluations more effectively to foster motivation in Chinese language learners.The findings indicate that both feedback methods have unique strengths,and a balanced approach may optimize learning outcomes.

Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Aspect-Based Sentiment Analysis(ABSA)is one of the essential research in the field of Natural Language Processing(NLP),of which Aspect Sentiment Quad Prediction(ASQP)is a novel and complete subtask.ASQP aims to accurately recognize the sentiment quad in the target sentence,which includes the aspect term,the aspect category,the corresponding opinion term,and the sentiment polarity of opinion.Nevertheless,existing approaches lack knowledge of the sentence’s syntax,so despite recent innovations in ASQP,it is poor for complex cyber comment processing.Also,most research has focused on processing English text,and ASQP for Chinese text is almost non-existent.Chinese usage is more casual than English,and individual characters contain more information.We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies.In this framework,part of speech(POS)and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure.Besides,we design a relation extraction module,which provides a bridge for the overall extraction of the framework.A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1.Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

According to BBC News,online hate speech increased by 20%during the COVID-19 pandemic.Hate speech from anonymous users can result in psychological harm,including depression and trauma,and can even lead to suicide.Malicious online comments are increasingly becoming a social and cultural problem.It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level.To achieve a healthy and safe Internet environment,studies should focus on institutional and technical topics.The detection of toxic comments can create a safe online environment.In this study,to detect malicious comments,we used approxi-mately 9,400 examples of hate speech from a Korean corpus of entertainment news comments.We developed toxic comment classification models using supervised learning algorithms,including decision trees,random forest,a support vector machine,and K-nearest neighbors.The proposed model uses random forests to classify toxic words,achieving an F1-score of 0.94.We analyzed the trained model using the permutation feature importance,which is an explanatory machine learning method.Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea.Using this research methodology,the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean.

Eleven “Opinion Editorials” were published since its introduction

Opinion Editorials(Op-Ed)was introduced in 2017 as a new category,and is attracting the attention of the international polar community.One scientist from School of Environment,Education and Development,the University of Manchester gave the comments that this is a most welcome initiative.Op-Ed can be a statement of the opinion of a named author or authors,with a maximum of 1500 words(a maximum length of 2 printed pages).It will provide a forum for comment on the status and/or deficiencies on any aspect of polar science,reflect on recent innovation or progress,promote internationally collaborative polar science projects,etc.It will be directly reviewed by EICs with final decision without peer review by expert referrers.Up to now,eleven Op-Eds have already been published on APS.

Customized Cosmetics: A Concept or the Future?

On April 10,2023,the Shanghai Municipal Medical Products Administration issued a notice on the public consultation of the“Shanghai Pudong New Area General Cosmetics On-site Personalized Service Review Rules(for trial implementation)”(draft for comment),marking the public call for comments from April 3 to May 2,2023.

A Study on the Acceptance of the English Translation of Romance of the Three Kingdoms by Overseas Readers Based on Python Data Analysis Technology

Paperless reading has become a prevalent trend among global readers,leading to the accumulation of vast amounts of reading data on numerous book websites.This offers new perspectives for studying translated works.This paper utilizes Python-based data processing technology to collect and analyze reader reviews of Romance of the Three Kingdoms on Amazon and Goodreads,presenting trends in review volume,word cloud maps,and readers’emotional attitudes in a quantitative manner.The findings indicate that overseas readers generally exhibit a positive emotional tendency towards Romance of the Three Kingdoms and recognize its cultural value.However,negative opinions do exist,focusing on aspects of the book’s quality,such as printing quality and proofreading.These results provide valuable insights for the foreign translation of canonical texts.

Quantum Physics Has a New, and Remarkable, Expansion

Canonical quantization has taught us great things. A common example is that of the harmonic oscillator, which is like swinging a ball on a string back and forth. However, the half-harmonic oscillator blocks the ball at the bottom and then it quickly bounces backwards. This second model cannot be correctly solved using canonical quantization. Now, there is an expansion of quantization, called affine quantization, that can correctly solve the half-harmonic oscillator, and offers correct solutions to a grand collection of other problems, which even reaches to field theory and gravity. This paper has been designed to introduce affine quantization: what it is, and what it can do.

Critical Discourse Analysis Based on Halliday’s Systemic Functional Linguistics: Taking The Economist’s First Commentation on the End of China’s Zero-COVID Policy as an Example

Based on Halliday’s systemic functional grammar,especially the ideational function,this research aims at disclosing the hidden ideologies and values of the seemingly objective news reports on China’s COVID-19 policies in The Economist.Transitivity,voice,and nominalization are the major analytical subjects.After China lifted the zero-COVID policy,western media began criticizing China’s lack of data sharing,with some misinformation and misleading reports.The denouncement of inertness and reluctance to fight against the pandemic disclaim the Chinese government’s efforts and depreciate China’s image.China is portrayed as the villain and destroyer of people’s health worldwide.Meanwhile,they also hold a hesitant attitude toward China’s diplomacy.The re-engaging with foreign countries and travel restrictions have been described as imprudent and rushed actions.They also consider China as the fuse of contradiction in the United Nations.What is overt is their view of breaking up China.

涎腺肿瘤的诊断和治疗

涎腺肿瘤临床常见,很多病例缺乏规范性治疗,误诊误治的病例不少。现就临床常遇到的一些问题和同道们共同探讨。1涎腺肿瘤的诊断涎腺肿瘤以发生在腮腺最常见,其次是下颌下腺。很多患者因无痛性肿块就诊,没有任何症状。临床检查为腺内界限清楚且活动的肿块,无压痛,这是良性肿瘤的典型表现。但是也有不少低度恶性肿瘤也呈此种表现,临床上很难确定。

躯体化障碍的诊断和性质

对于躯体化障碍，目前有两种诊断标准：一种是美国精神病学学会《精神障碍诊断与统计手册第4版》（Diagnostic and Statistical Manual of Mental Disorders，DSM—IV）”。的诊断标准，一种是《国际疾病分类》第10版（International Classification of Diseases，ICD-10）的诊断要点（笔者按，

简论十六国北朝时期的敦煌大族与佛教

本文把十六国北朝时期的敦煌大族与佛教问题分为三期。汉晋之际,初居敦煌的大族主要独尊儒家文化,他们并未真正信仰佛教;十六国时期,他们在尊奉传统儒家文化的同时,开始接受佛教,在思想信仰方面儒释兼行;北朝时期,其佛教信仰有了长足发展,且有压倒儒学之势。这个时期,敦煌大族的思想信仰和文化代表了敦煌宗教文化的发展方向。