DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without...DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step,demonstrates remarkable reasoning capabilities of performing a wide range of tasks.DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries.Users possess divergent viewpoints regarding advanced models like DeepSeek,posting both their merits and shortcomings across several social media platforms.This research presents a new framework for predicting public sentiment to evaluate perceptions of DeepSeek.To transform the unstructured data into a suitable manner,we initially collect DeepSeek-related tweets from Twitter and subsequently implement various preprocessing methods.Subsequently,we annotated the tweets utilizing the Valence Aware Dictionary and sentiment Reasoning(VADER)methodology and the lexicon-driven TextBlob.Next,we classified the attitudes obtained from the purified data utilizing the proposed hybrid model.The proposed hybrid model consists of long-term,shortterm memory(LSTM)and bidirectional gated recurrent units(BiGRU).To strengthen it,we include multi-head attention,regularizer activation,and dropout units to enhance performance.Topic modeling employing KMeans clustering and Latent Dirichlet Allocation(LDA),was utilized to analyze public behavior concerning DeepSeek.The perceptions demonstrate that 82.5%of the people are positive,15.2%negative,and 2.3%neutral using TextBlob,and 82.8%positive,16.1%negative,and 1.2%neutral using the VADER analysis.The slight difference in results ensures that both analyses concur with their overall perceptions and may have distinct views of language peculiarities.The results indicate that the proposed model surpassed previous state-of-the-art approaches.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects in...As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentime...Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.展开更多
This paper investigates the application of Natural Language Processing (NLP) in AI interaction design for virtual experiences. It analyzes the impact of various interaction methods on user experience, integrating Virt...This paper investigates the application of Natural Language Processing (NLP) in AI interaction design for virtual experiences. It analyzes the impact of various interaction methods on user experience, integrating Virtual Reality (VR) and Augmented Reality (AR) technologies to achieve more natural and intuitive interaction models through NLP techniques. Through experiments and data analysis across multiple technical models, this study proposes an innovative design solution based on natural language interaction and summarizes its advantages and limitations in immersive experiences.展开更多
Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusia...Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.展开更多
在自然语言处理(Natural Language Processing,NLP)领域,后门攻击已成为现代NLP应用的重大威胁,严重影响系统的安全性与可靠性。尽管文本领域已提出多种防御策略,但在不接触中毒数据集也不参与后门训练过程时,面对复杂的攻击场景,现有...在自然语言处理(Natural Language Processing,NLP)领域,后门攻击已成为现代NLP应用的重大威胁,严重影响系统的安全性与可靠性。尽管文本领域已提出多种防御策略,但在不接触中毒数据集也不参与后门训练过程时,面对复杂的攻击场景,现有方法仍难以有效应对。为此,提出一种基于机器遗忘的文本后门攻击防御方法NLPShield。该方法仅需少量干净样本,通过基于错误标注的训练和干净神经元剪枝两个关键阶段,实现对文本后门攻击的有效防御。实验在SST-2和AGNews数据集上进行,结果显示,在保持较高干净准确率的情况下,NLPShield方法相较于现有最先进基线防御方法,平均能将攻击成功率降低24.83%。这表明NLPShield方法能显著提升多种后门攻击的防御效果,切实有效地缓解文本后门攻击。展开更多
航空装备各系统复杂性高,交联性强,利用传统的故障预测方法难以判断和预测故障部位。针对此类问题,文章提出了一种基于自然语言处理(Natural Language Processing,NLP)和案例推理(Case-Based Reasoning,CBR)的航空装备故障智能预测方法...航空装备各系统复杂性高,交联性强,利用传统的故障预测方法难以判断和预测故障部位。针对此类问题,文章提出了一种基于自然语言处理(Natural Language Processing,NLP)和案例推理(Case-Based Reasoning,CBR)的航空装备故障智能预测方法,首先进行源案例库特征提取,进行数据清洗,再采用了自然语言处理(Natural Language Processing,NLP)获取源案例库中故障现象描述的词向量表示,然后使用文本相似度检测得到目标案例与源案例的相似度,并结合CBR方法进行组合预测,得出具体排故方案,最后,通过使用Python软件进行实例分析应用,验证了该模型的正确性及该方法的实用性。展开更多
Topic modeling is a fundamental technique of content analysis in natural language processing,widely applied in domains such as social sciences and finance.In the era of digital communication,social scientists increasi...Topic modeling is a fundamental technique of content analysis in natural language processing,widely applied in domains such as social sciences and finance.In the era of digital communication,social scientists increasingly rely on large-scale social media data to explore public discourse,collective behavior,and emerging social concerns.However,traditional models like Latent Dirichlet Allocation(LDA)and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets,especially in complex non-English languages like Chinese.This paper presents Generative Language Model Topic(GLMTopic)a novel hybrid topic modeling framework leveraging the capabilities of large language models,designed to support social science research by uncovering coherent and interpretable themes from Chinese social media platforms.GLMTopic integrates Adaptive Community-enhanced Graph Embedding for advanced semantic representation,Uniform Manifold Approximation and Projection-based(UMAP-based)dimensionality reduction,Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)clustering,and large language model-powered(LLM-powered)representation tuning to generate more contextually relevant and interpretable topics.By reducing dependence on extensive text preprocessing and human expert intervention in post-analysis topic label annotation,GLMTopic facilitates a fully automated and user-friendly topic extraction process.Experimental evaluations on a social media dataset sourced from Weibo demonstrate that GLMTopic outperforms Latent Dirichlet Allocation(LDA)and BERTopic in coherence score and usability with automated interpretation,providing a more scalable and semantically accurate solution for Chinese topic modeling.Future research will explore optimizing computational efficiency,integrating knowledge graphs and sentiment analysis for more complicated workflows,and extending the framework for real-time and multilingual topic modeling.展开更多
We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of t...We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.展开更多
This critical review provides an in-depth analysis of Large Language Models(LLMs),encompassing their foundational principles,diverse applications,and advanced training methodologies.We critically examine the evolution...This critical review provides an in-depth analysis of Large Language Models(LLMs),encompassing their foundational principles,diverse applications,and advanced training methodologies.We critically examine the evolution from Recurrent Neural Networks(RNNs)to Transformer models,highlighting the significant advancements and innovations in LLM architectures.The review explores state-of-the-art techniques such as in-context learning and various fine-tuning approaches,with an emphasis on optimizing parameter efficiency.We also discuss methods for aligning LLMs with human preferences,including reinforcement learning frameworks and human feedback mechanisms.The emerging technique of retrieval-augmented generation,which integrates external knowledge into LLMs,is also evaluated.Additionally,we address the ethical considerations of deploying LLMs,stressing the importance of responsible and mindful application.By identifying current gaps and suggesting future research directions,this review provides a comprehensive and critical overview of the present state and potential advancements in LLMs.This work serves as an insightful guide for researchers and practitioners in artificial intelligence,offering a unified perspective on the strengths,limitations,and future prospects of LLMs.展开更多
With the rapid development of natural language processing(NLP)and machine learning technology,applying large language models(LLMs)in the financial field shows a significant growth trend.This paper systematically revie...With the rapid development of natural language processing(NLP)and machine learning technology,applying large language models(LLMs)in the financial field shows a significant growth trend.This paper systematically reviews the development status,main applications,challenges,and future development direction of LLMs in the financial field.Financial Language models(FinLLMs)have been successfully applied to many scenarios,such as sentiment analysis,automated trading,risk assessment,etc.,through deep learning architectures such as BERT,Llama,and domain data fine-tuning.However,issues such as data privacy,model interpretability,and ethical governance still pose constraints to their widespread application.Future research should focus on improving model performance,addressing bias issues,strengthening privacy protection,and establishing a sound regulatory framework to ensure the healthy development of LLMs in the financial sector.展开更多
This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.展开更多
文摘DeepSeek Chinese artificial intelligence(AI)open-source model,has gained a lot of attention due to its economical training and efficient inference.DeepSeek,a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step,demonstrates remarkable reasoning capabilities of performing a wide range of tasks.DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries.Users possess divergent viewpoints regarding advanced models like DeepSeek,posting both their merits and shortcomings across several social media platforms.This research presents a new framework for predicting public sentiment to evaluate perceptions of DeepSeek.To transform the unstructured data into a suitable manner,we initially collect DeepSeek-related tweets from Twitter and subsequently implement various preprocessing methods.Subsequently,we annotated the tweets utilizing the Valence Aware Dictionary and sentiment Reasoning(VADER)methodology and the lexicon-driven TextBlob.Next,we classified the attitudes obtained from the purified data utilizing the proposed hybrid model.The proposed hybrid model consists of long-term,shortterm memory(LSTM)and bidirectional gated recurrent units(BiGRU).To strengthen it,we include multi-head attention,regularizer activation,and dropout units to enhance performance.Topic modeling employing KMeans clustering and Latent Dirichlet Allocation(LDA),was utilized to analyze public behavior concerning DeepSeek.The perceptions demonstrate that 82.5%of the people are positive,15.2%negative,and 2.3%neutral using TextBlob,and 82.8%positive,16.1%negative,and 1.2%neutral using the VADER analysis.The slight difference in results ensures that both analyses concur with their overall perceptions and may have distinct views of language peculiarities.The results indicate that the proposed model surpassed previous state-of-the-art approaches.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
文摘As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
文摘Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.
文摘This paper investigates the application of Natural Language Processing (NLP) in AI interaction design for virtual experiences. It analyzes the impact of various interaction methods on user experience, integrating Virtual Reality (VR) and Augmented Reality (AR) technologies to achieve more natural and intuitive interaction models through NLP techniques. Through experiments and data analysis across multiple technical models, this study proposes an innovative design solution based on natural language interaction and summarizes its advantages and limitations in immersive experiences.
文摘Although the 2022 cryptocurrency market crash prompted despair among investors,the rallying cry,“wagmi”(We’re all gonna make it.)emerged among cryptocurrency enthusiasts in the aftermath.Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors?Using natural language processing techniques applied to Twitter data,this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors.The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors.In particular,cryptocurrency enthusiasts’tweets became more neutral and,surprisingly,less negative.This result appears to be primarily driven by a deliberate,collectivist effort to promote positivity within the cryptocurrency community(“wagmi”).Considering the more nuanced emotional content of tweets,it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors.Moreover,cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash,with a relative increase in tweet frequency of approximately one tweet per day.An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.
文摘在自然语言处理(Natural Language Processing,NLP)领域,后门攻击已成为现代NLP应用的重大威胁,严重影响系统的安全性与可靠性。尽管文本领域已提出多种防御策略,但在不接触中毒数据集也不参与后门训练过程时,面对复杂的攻击场景,现有方法仍难以有效应对。为此,提出一种基于机器遗忘的文本后门攻击防御方法NLPShield。该方法仅需少量干净样本,通过基于错误标注的训练和干净神经元剪枝两个关键阶段,实现对文本后门攻击的有效防御。实验在SST-2和AGNews数据集上进行,结果显示,在保持较高干净准确率的情况下,NLPShield方法相较于现有最先进基线防御方法,平均能将攻击成功率降低24.83%。这表明NLPShield方法能显著提升多种后门攻击的防御效果,切实有效地缓解文本后门攻击。
文摘航空装备各系统复杂性高,交联性强,利用传统的故障预测方法难以判断和预测故障部位。针对此类问题,文章提出了一种基于自然语言处理(Natural Language Processing,NLP)和案例推理(Case-Based Reasoning,CBR)的航空装备故障智能预测方法,首先进行源案例库特征提取,进行数据清洗,再采用了自然语言处理(Natural Language Processing,NLP)获取源案例库中故障现象描述的词向量表示,然后使用文本相似度检测得到目标案例与源案例的相似度,并结合CBR方法进行组合预测,得出具体排故方案,最后,通过使用Python软件进行实例分析应用,验证了该模型的正确性及该方法的实用性。
基金funded by the Natural Science Foundation of Fujian Province,China,grant No.2022J05291.
文摘Topic modeling is a fundamental technique of content analysis in natural language processing,widely applied in domains such as social sciences and finance.In the era of digital communication,social scientists increasingly rely on large-scale social media data to explore public discourse,collective behavior,and emerging social concerns.However,traditional models like Latent Dirichlet Allocation(LDA)and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets,especially in complex non-English languages like Chinese.This paper presents Generative Language Model Topic(GLMTopic)a novel hybrid topic modeling framework leveraging the capabilities of large language models,designed to support social science research by uncovering coherent and interpretable themes from Chinese social media platforms.GLMTopic integrates Adaptive Community-enhanced Graph Embedding for advanced semantic representation,Uniform Manifold Approximation and Projection-based(UMAP-based)dimensionality reduction,Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)clustering,and large language model-powered(LLM-powered)representation tuning to generate more contextually relevant and interpretable topics.By reducing dependence on extensive text preprocessing and human expert intervention in post-analysis topic label annotation,GLMTopic facilitates a fully automated and user-friendly topic extraction process.Experimental evaluations on a social media dataset sourced from Weibo demonstrate that GLMTopic outperforms Latent Dirichlet Allocation(LDA)and BERTopic in coherence score and usability with automated interpretation,providing a more scalable and semantically accurate solution for Chinese topic modeling.Future research will explore optimizing computational efficiency,integrating knowledge graphs and sentiment analysis for more complicated workflows,and extending the framework for real-time and multilingual topic modeling.
文摘We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.
文摘This critical review provides an in-depth analysis of Large Language Models(LLMs),encompassing their foundational principles,diverse applications,and advanced training methodologies.We critically examine the evolution from Recurrent Neural Networks(RNNs)to Transformer models,highlighting the significant advancements and innovations in LLM architectures.The review explores state-of-the-art techniques such as in-context learning and various fine-tuning approaches,with an emphasis on optimizing parameter efficiency.We also discuss methods for aligning LLMs with human preferences,including reinforcement learning frameworks and human feedback mechanisms.The emerging technique of retrieval-augmented generation,which integrates external knowledge into LLMs,is also evaluated.Additionally,we address the ethical considerations of deploying LLMs,stressing the importance of responsible and mindful application.By identifying current gaps and suggesting future research directions,this review provides a comprehensive and critical overview of the present state and potential advancements in LLMs.This work serves as an insightful guide for researchers and practitioners in artificial intelligence,offering a unified perspective on the strengths,limitations,and future prospects of LLMs.
文摘With the rapid development of natural language processing(NLP)and machine learning technology,applying large language models(LLMs)in the financial field shows a significant growth trend.This paper systematically reviews the development status,main applications,challenges,and future development direction of LLMs in the financial field.Financial Language models(FinLLMs)have been successfully applied to many scenarios,such as sentiment analysis,automated trading,risk assessment,etc.,through deep learning architectures such as BERT,Llama,and domain data fine-tuning.However,issues such as data privacy,model interpretability,and ethical governance still pose constraints to their widespread application.Future research should focus on improving model performance,addressing bias issues,strengthening privacy protection,and establishing a sound regulatory framework to ensure the healthy development of LLMs in the financial sector.
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
