From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).

Survival strategies for family-run homestays: analyzing user reviews through text mining

Online booking of homestays through e-travel portals is based on the virtual brand and perception,which are largely affected by user-generated electronic word-of-mouth(eWOM).With the objective of mining actionable insights from eWOM,this study conducted opinion mining for homestays located in four thematic areas of Kerala.Accordingly,various techniques have been deployed,such as sentiment and emotional analyses,topic modeling,and clustering methods.Key themes revealed from topic modeling were breakfast,facilities provided,ambience,bathroom,cleanliness,hospitality exhibited,and satisfaction with the host.A lasso logistic regression-based predictive binary text classification model(with 97.6%accuracy)for homestay recommendations was developed.Our findings and predictive model have implications for managers and homestay owners in devising appropriate marketing strategies and improving their overall guest experience.

基于改进TextRank的科技文本关键词抽取方法

针对科技文本关键词抽取任务中抽取出现次数少但能较好表达文本主旨的词语效果差的问题,提出一种基于改进TextRank的关键词抽取方法。首先,利用词语的词频-逆文档频率(TF-IDF)统计特征和位置特征优化共现图中词语间的概率转移矩阵,通过迭代计算得到词语的初始得分;然后,利用K-Core(K-Core decomposition)算法挖掘KCore子图得到词语的层级特征,利用平均信息熵特征衡量词语的主题表征能力;最后,在词语初始得分的基础上融合层级特征和平均信息熵特征,从而确定关键词。实验结果表明,在公开数据集上,与TextRank方法和OTextRank(Optimized TextRank)方法相比,所提方法在抽取不同关键词数量的实验中,F1均值分别提高了6.5和3.3个百分点;在科技服务项目数据集上,与TextRank方法和OTextRank方法相比,所提方法在抽取不同关键词数量的实验中,F1均值分别提高了7.4和3.2个百分点。实验结果验证了所提方法抽取出现频率低但较好表达文本主旨关键词的有效性。

基于Photo Sphere Viewer的轻量化可量测街景系统研究

随着新型基础测绘体系发展,测绘技术和装备不断升级,大量街景数据随着车载激光扫描工作一并被采集。针对该数据路线随机性大、密集程度高等特点,以及传统街景地图系统插件受限、扩展性不强、无法量测等局限,本文设计了基于Photo Sphere Viewer的轻量化可量测街景系统,在开源插件的基础上实现了对其组件的扩充、实现了行进方向、地图交互等系统功能的设计优化,进一步实现了基于深度图的街景地图量测功能。生产作业队伍对系统的使用结果表明,该系统可以辅助生产作业队伍进行属性采集、纹理更新、数据检查等多项工作,操作灵活便捷,且具有较强的可扩展性,便于基于生产需求进行改进调整,提高了生产效率,为新型基础测绘成果进一步深化应用提供了良好的借鉴作用。

Review of Text Classification Methods on Deep Learning

Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,data sparsity,limited generalization ability and so on.Based on deep learning text classification,this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based(CNN-Based),Recurrent Neural Network-Based(RNN-based),Attention Mechanisms-Based and so on.Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets.The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data.In this paper,we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing,distributed representation of text,text classification model construction based on deep learning and performance evaluation.

Comprehensive review of text‑mining applications in finance

Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.

我国数据素养教育研究的发展趋势及优化策略探讨--基于VOSviewer工具文本分析视角

随着网络强国、数字中国战略的全面实施,数据素养教育引发了研究群体的广泛关注,成为深化数据融合应用,释放数据红利的重要手段。本文借助文本分析技术,研究分析中国知网中以“数据素养教育”为主题的1331篇文献,围绕发文量、发文机构、资助基金、关键词、研究主题等构建我国数据素养教育研究知识图谱,阐述我国数据素养教育研究领域的发展脉络及现状,基于资源依赖理论提出我国数据素养教育目前存在的问题,并给出优化对策。

多视图融合DJ-TextRCNN的古籍文本主题推荐研究

传统编目分类和规则匹配方法存在工作效能低、过度依赖专家知识、缺乏对古籍文本自身语义的深层次挖掘、编目主题边界模糊、较难实现对古籍文本领域主题的精准推荐等问题。为此,本文结合古籍语料特征探究如何实现精准推荐符合研究者需求的文本主题内容的方法,以推动数字人文研究的进一步发展。首先,选取本课题组前期标注的古籍语料数据进行主题类别标注和视图分类;其次,构建融合BERT(bidirectional encoder representation from transformers)预训练模型、改进卷积神经网络、循环神经网络和多头注意力机制的语义挖掘模型;最后,融入“主体-关系-客体”多视图的语义增强模型,构建DJ-TextRCNN(DianJi-recurrent convolutional neural networks for text classification)模型实现对典籍文本更细粒度、更深层次、更多维度的语义挖掘。研究结果发现,DJ-TextRCNN模型在不同视图下的古籍主题推荐任务的准确率均为最优。在“主体-关系-客体”视图下,精确率达到88.54%,初步实现了对古籍文本的精准主题推荐,对中华文化深层次、细粒度的语义挖掘具有一定的指导意义。

一种利用词典扩展数据库模式信息的Text2SQL方法

现有Text2SQL方法严重依赖表名和列名在自然语言查询中的显式提及,在同物异名的实际应用场景中准确率急剧下降.此外,这些方法仅仅依赖数据库模式捕捉数据库建模的领域知识,而数据库模式作为结构化的元数据,其表达领域知识的能力是非常有限的,即使有经验的程序员也很难仅从数据库模式完全领会该数据库建模的领域知识,因此程序员必须依赖详细的数据库设计文档才能构造SQL语句以正确地表达特定的查询.为此,本文提出一种利用词典扩展数据库模式信息的Text2SQL方法,该方法从数据库表名和列名解析出其中的单词或短语,查询词典获取这些单词或短语的语义解释,将这些解释看成是相应表名或列名的扩展内容,与表名、列名及其他数据库模式信息(主键、外键等)相结合,作为模型的输入,从而使模型能够更全面地学习数据库建模的应用领域知识.在Spider-syn和Spider数据集上进行的实验说明了所提出方法的有效性,即使自然语言查询中使用的表名和列名与数据库模式中对应的表名和列名完全不同,本文方法也能够得到较好的SQL翻译结果,明显优于最新提出的抗同义词替换攻击的方法.

LRV: A Tool for Academic Text Visualization to Support theLiterature Review Process

Text visualization is concerned with the representation of text in a graphicalform to facilitate comprehension of large textual data. Its aim is to improve the ability tounderstand and utilize the wealth of text-based information available. An essential task inany scientific research is the study and review of previous works in the specified domain,a process that is referred to as the literature survey process. This process involves theidentification of prior work and evaluating its relevance to the research question. With theenormous number of published studies available online in digital form, this becomes acumbersome task for the researcher. This paper presents the design and implementationof a tool that aims to facilitate this process by identifying relevant work and suggestingclusters of articles by conceptual modeling, thus providing different options that enablethe researcher to visualize a large number of articles in a graphical easy-to-analyze form.The tool helps the researcher in analyzing and synthesizing the literature and building aconceptual understanding of the designated research area. The evaluation of the toolshows that researchers have found it useful and that it supported the process of relevantwork analysis given a specific research question, and 70% of the evaluators of the toolfound it very useful.

Construction of Complex Intervention in Traditional Chinese Medicine (TCM): A Preliminary Methodological Study Based on Text Mining and Interviewing Method

Aim: To explore and analyze the feasibility of establishing a program of complex intervention in Traditional Chinese Medicine (TCM) based on Text Mining and Interviewing method. Methods: According to MRC, Constructing the program of complex intervention in TCM by Text Mining and Interviewing method should include 4 steps: 1) establishment of interview framework via normalization of extraction of ancient documents and Effectiveness of collection of modern periodical literatures;2) materialization of interview outline based on Focus Group Interview;3) rudimentary construction of complex intervention program based on Semi-structured Interview;4) evaluation of curative effect of complex intervention. Conclusions: It is feasible and significative to establish a program of complex intervention in TCM based on Text Mining and Interviewing method.

基于DAN与FastText的藏文短文本分类研究

随着藏文信息不断融入社会生活,越来越多的藏文短文本数据存在网络平台上。针对传统分类方法在藏文短文本上分类性能低的问题,文中提出了一种基于DAN-FastText的藏文短文本分类模型。该模型使用FastText网络在较大规模的藏文语料上进行无监督训练获得预训练的藏文音节向量集,使用预训练的音节向量集将藏文短文本信息转化为音节向量,把音节向量送入DAN(Deep Averaging Networks)网络并在输出阶段融合经过FastText网络训练的句向量特征,最后通过全连接层和softmax层完成分类。在公开的TNCC(Tibetan News Classification Corpus)新闻标题数据集上所提模型的Macro-F1是64.53%,比目前最好评测结果TiBERT模型的Macro-F1得分高出2.81%,比GCN模型的Macro-F1得分高出6.14%,融合模型具有较好的藏文短文本分类效果。

A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods

With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.

基于SWPF2vec和DJ-TextRCNN的古籍文本主题分类研究

以编目分类和规则匹配为主的古籍文本主题分类方法存在工作效能低、专家知识依赖性强、分类依据单一化、古籍文本主题自动分类难等问题。对此,本文结合古籍文本内容和文字特征,尝试从古籍内容分类得到符合研究者需求的主题,推动数字人文研究范式的转型。首先,参照东汉古籍《说文解字》对文字的分析方式,以前期标注的古籍语料数据集为基础,构建全新的“字音(说)-原文(文)-结构(解)-字形(字)”四维特征数据集。其次,设计四维特征向量提取模型(speaking,word,pattern,and font to vector,SWPF2vec),并结合预训练模型实现对古籍文本细粒度的特征表示。再其次,构建融合卷积神经网络、循环神经网络和多头注意力机制的古籍文本主题分类模型(dianji-recurrent convolutional neural networks for text classification,DJ-TextRCNN)。最后,融入四维语义特征,实现对古籍文本多维度、深层次、细粒度的语义挖掘。在古籍文本主题分类任务上,DJ-TextRCNN模型在不同维度特征下的主题分类准确率均为最优,在“说文解字”四维特征下达到76.23%的准确率,初步实现了对古籍文本的精准主题分类。

Overview of the J-TEXT progress on RMP and disruption physics

The J-TEXT tokamak has been operated for ten years since its first plasma obtained at the end of 2007. The diagnostics development and main modulation systems, i.e. resonant magnetic perturbation （RMP） systems and massive gas injection （MGI） systems, will be introduced in this paper. Supported by these efforts, J-TEXT has contributed to research on several topics, especially on RMP physics and disruption mitigation. Both experimental and theoretical research show that RMP could lock, suppress or excite the tearing modes, depending on the RMP amplitude, frequency difference between RMP and rational surface rotation, and initial stabilities. The plasma rotation, particle transport and operation region are influenced by the RMP. Utilizing the MGI valves, disruptions have been mitigated with pure He, pure Ne, and a mixture of He and Ar （9：1）. A significant runaway current plateau could be generated with moderate amounts of Ar injection. The RMP has been shown to suppress the generation of runaway current during disruptions.

基于单片机和LabVIEW的无线温度监测与控制系统设计

本文设计了一个基于STC89C52单片机和LabVIEW的无线温度监测与控制系统.该系统采用上下位机的结构,其中下位机通过STC89C52单片机控制DS18B20采集实时温度,并利用控制模块实现加热或降温,同时,通过蓝牙模块实现与上位机的无线通信;上位机以LabVIEW作为开发平台,实现对温度的实时显示、处理、存储、阈值设定和温度控制等功能.该系统电路设计简单、成本低、扩展性强、人机界面友好,可用于智能大棚的温度控制等需要温度检测的场所,具有广阔的应用前景.

基于BERT和TextCNN的智能制造成熟度评估方法

随着智能制造2025目标的临近,企业为了解自身能力水平纷纷加入到智能制造成熟度评估的行列中。然而,由于智能制造成熟度评估标准的复杂性,企业缺乏其对行业水平的了解,导致企业贸然申请,浪费自身时间的同时又占用大量评估资源。鉴于此,设计了一种新的评估流程,采用文本处理算法对整个评估过程进行了重构,通过利用国标文件中智能制造成熟度评估标准,将其作为训练集,采用基于预训练语言模型与文本神经网络(BERT+TextCNN)相结合的智能评估算法代替人工评估。在真实的企业智能制造数据集上的验证表明,当BERT+TextCNN评估模型在卷积核为[2,3,4]、迭代次数为6次、学习率为3e-5时,对智能制造成熟度进行评估,准确率达到85.32%。这表明所设计的评估方法能够较准确地帮助企业完成智能制造成熟度自评估,有助于企业了解自身智能制造能力水平,制定正确的发展方向。

A Brief Overview of Cohesion in Text

In a text,there are many ties used to link the language together.Cohesion and coherence are the two important factors.In order to create the coherence in the discourse,writers and speakers have to use a lot of cohesive devices within clause and between clause complexes,including reference,ellipsis,substitution and lexical cohesion.The analysis of cohesion facilitates the use of language.

Research on the Public Perception of Yangzhou Historical and Cultural Districts Based on Network Review Text Analysis

The historical and cultural districts of a city serve as important cultural heritage and tourism resources.This paper focused on four such districts in Yangzhou and performed semantic analysis on online public comments using ROST CM6 software.According to the high frequency words,attention preference of district site elements,activities and feelings in Yangzhou historical and cultural districts were analyzed.Through the analysis of semantic network and public emotional tendency,the relationship between the protection and utilization of Yangzhou historical and cultural districts and the perception and demand of users were discussed,and some suggestions for the protection,utilization and renewal of historical and cultural districts were put forward.

Identifying multidisciplinary problems from scientific publications based on a text generation method

Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.