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Quantifying the Psychological Online Communities Considering the Relationship between COVID-19-Related Threat, Information Uncertainty, and Risk Perception

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natural HAZARDS REVIEW 2024年第3期25卷

作者： Lu, Liangdong Xu, Jia Wei, Jiuchang LeRon Shults, F. Hohai Univ Business Sch Nanjing 211100 Peoples R China Univ Sci & Technol China Affiliated Hosp 1 Anhui Prov Hosp 96 JinZhai Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China State Key Lab Fire Sci 96 JinZhai Rd Hefei 230026 Anhui Peoples R China Univ Agder Inst Global Dev & Planning N-4604 Kristiansand Norway

This study employed deep learning to analyze a substantial data set of 109.13 million COVID-19-related microblogs, leading to the construction of a specialized risk perception indicator dictionary. Employing this dictionary, we were able to capture the dynamic fluctuations in risk perception within online communities across various cities in real time. This approach highlighted the varying intensities of public response to the evolving crisis during the isolation and normalization stages of the pandemic. We observed that COVID-19-related transmission threat and information uncertainty significantly influenced public risk perception at different stages of the pandemic. Innovatively, our study quantifies public psychological resilience within online communities by examining the equilibrium between public risk perception and objective COVID-19-related risks. This equilibrium is conceptualized as the alignment of public perception with the evolving reality of COVID-19 threat and information. We investigated psychological resilience in two dimensions: adaptability, indicated by the extent of deviation from this equilibrium, and agility, reflected in the rate at which equilibrium is reestablished. Our study not only unveils new insights into the intricate relationship among public risk perception, the evolving risks, and psychological resilience but also offers empirical evidence to inform risk management strategies in online communities at different stages of a crisis. This research provides essential insights into how public perception and emotional responses during health crises like COVID-19 can be monitored and analyzed through social media data. By utilizing advanced analytical methods, including natural language processing (NLP) and panel vector error correction (PVEC) modeling, the study successfully quantified the psychological resilience of online communities. These methods allow for the real-time assessment of how communities adapt and respond to evolving risks

关键词： Threat Uncertainty Psychological resilience Risk perception natural language processing

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Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using language Model

Multimodal Self-Instruct: Synthetic Abstract Image and Visua...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhang, Wenqi Cheng, Zhenglin He, Yuanyu Wang, Mengna Shen, Yongliang Tan, Zeqi Hou, Guiyang He, Mingqian Ma, Yanna Lu, Weiming Zhuang, Yueting College of Computer Science and Technology Zhejiang University China Institute of Software Chinese Academy of Sciences China University of Shanghai for Science and Technology China

ISBN: (纸本)9798891761643

Although most current large multimodal models (LMMs) can already understand photos of natural scenes and portraits, their understanding of abstract images, e.g., charts, maps, or layouts, and visual reasoning capabilities remains quite *** often struggle with simple daily tasks, such as reading time from a clock, understanding a flowchart, or planning a route using a road *** light of this, we design a multi-modal self-instruct pipeline, utilizing large language models and their code capabilities to synthesize massive abstract images and visual reasoning instructions across daily *** strategy effortlessly creates a multimodal benchmark with 11,193 instructions for eight visual scenarios: charts, tables, simulated maps, dashboards, flowcharts, relation graphs, floor plans, and visual *** benchmark, constructed with simple lines and geometric elements, exposes the shortcomings of most advanced LMMs like Claude-3.5-Sonnet and GPT-4o in abstract image understanding, spatial relations reasoning, and visual element ***, to verify the quality of our synthetic data, we fine-tune an LMM using 62,476 synthetic chart, table and road map *** results demonstrate improved chart understanding and map navigation performance, and also demonstrate potential benefits for other visual reasoning *** code is available at: https://***/zwq2018/Multi-modal-Self-instruct. © 2024 Association for Computational Linguistics.

关键词： Benchmarking

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A natural language processing system for the efficient extraction of cell markers

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Cheng, Peng Peng, Yan Zhang, Xiao-Ling Chen, Sheng Fang, Bin-Bin Li, Yan-Ze Sun, Yi-Min CapitalBio Technol Mkt & Management Dept Beijing 100176 Peoples R China Natl Engn Res Ctr Beijing Biochip Technol Beijing 102206 Peoples R China

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://***/chengpeng1116/MarkerGeneBERT.

关键词： ScRNA-seq natural language processing Cell marker

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Single-Document Abstractive Text Summarization: A Systematic Literature Review

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ACM COMPUTING SURVEYS 2025年第3期57卷 1-37页

作者： Rao, Abishek Aithal, Shivani Singh, Sanjay Manipal Inst Technol Informat & Commun Technol Manipal India

text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document summarization to gain insights into the challenges, widely used datasets, evaluation metrics, approaches, and methods. This study reviews research articles published between 2011 and 2023 from popular electronic databases. In total, 226 journal and conference publications were included in this review. The in-depth analysis of these papers helps researchers understand the challenges, widely used datasets, evaluation metrics, approaches, and methods. This article identifies and discusses potential opportunities and directions along with a generic conceptual framework and guidelines on abstractive summarization models and techniques for research in abstractive text summarization.

关键词： CCS Concepts Information systems- Summarization Computing methodologies- natural language generation General and reference- Surveys and overviews

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Investigating the Role and Impact of Disfluency on Summarization

Investigating the Role and Impact of Disfluency on Summariza...

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2023 conference on empirical methods in natural language processing, EMNLP 2023

作者： Nathan, Varun Kumar, Ayush Vepa, Jithendra Observe.AI Bangalore India

Contact centers handle both chat and voice calls for the same domain. As part of their workflow, it is a standard practice to summarize the conversations once they conclude. A significant distinction between chat and voice communication lies in the presence of disfluencies in voice calls, such as repetitions, restarts, and replacements. These disfluencies are generally considered noise for downstream natural language understanding (NLU) tasks. While a separate summarization model for voice calls can be trained in addition to chat specific model for the same domain, it requires manual annotations for both the channels and adds complexity arising due to maintaining two models. Therefore, it's crucial to investigate if a model trained on fluent data can handle disfluent data effectively. While previous research explored impact of disfluency on question-answering and intent detection, its influence on summarization is inadequately studied. Our experiments reveal up to 6.99-point degradation in Rouge-L score, along with reduced fluency, consistency, and relevance when a fluent-trained model handles disfluent data. Replacement disfluencies have the highest negative impact. To mitigate this, we examine Fused-Fine Tuning by training the model with a combination of fluent and disfluent data, resulting in improved performance on both public and real-life datasets. Our work highlights the significance of incorporating disfluency in training summarization models and its advantages in an industrial setting. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

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Dialogic Process Analysis in natural language processing: An Attempt to Describe the Sense of Reality and Meaning of Textdata 16th

Dialogic Process Analysis in Natural Language Processing: An...

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16th International conference on Statistical Analysis of Textual Data (JADT)

作者： Turchi, Gian Piero Moro, Christian Neri, Jessica Orru, Luisa Univ Padua FISPPA Dept Padua Italy

ISBN: (纸本)9783031559167;9783031559174

Up until now, in the field of natural language processing and Computational Text Analysis methods (CTAM) most studies focused on logical-grammatical analysis or, more recently, on content and sentiment analysis. However, there is still limited reference to the role of the discursive process: that is, how language's use shapes the reality of sense in which we live in. But how can we gain a deep knowledge and understanding of the sense of what is conveyed by a text? In order to investigate the process of sense's reality configuration, we introduce Dialogic Process Analysis. Starting from the formalization of 24 rules of natural language's use of transversal to every idiom, called Discursive Repertories, Dialogic Process Analysis allows to describe how discursive processes unravel and to trace precisely the elements that generate each specific sense's reality, which may be different even when contents and meanings are the same. Although researchers are able to denominate the Discursive Repertories, performing such a task requires specific and complex analysis expertise: that is why the application of Machine Learning models can lighten these problems. Thus, in this work we present the Dialogic Process Analysis research programme, its experimentations and results in the definition of its own Machine Learning model for textual data analysis and its future lines of development.

关键词： natural language processing Computational text analysis methods Dialogic process analysis

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PhiloGPT: A Philology-Oriented Large language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study

PhiloGPT: A Philology-Oriented Large Language Model for Anci...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhang, Yuqing He, Baoyi Chen, Yihan Li, Hangqi Han, Yue Zhang, Shengyu Dou, Huaiyong Yan, Junchi Liu, Zemin Zhang, Yongquan Wu, Fei Zhejiang University Hangzhou China Shanghai Institute for Advanced Study of Zhejiang University Shanghai China Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China

ISBN: (纸本)9798891761643

Philology, the study of ancient manuscripts, demands years of professional training in extensive knowledge memorization and manual textual retrieval. Despite these requirements align closely with strengths of recent successful Large language Models (LLMs), the scarcity of high-quality, specialized training data has hindered direct applications. To bridge this gap, we curated the PhiloCorpus-ZH, a rich collection of ancient Chinese texts spanning a millennium with 30 diverse topics, including firsthand folk copies. This corpus facilitated the development of PhiloGPT, the first LLM tailored for discovering ancient Chinese manuscripts. To effectively tackle complex philological tasks like restoration, attribution, and linguistic analysis, we introduced the PhiloCoP framework. Modeled on the analytical patterns of philologists, PhiloCoP enhances LLM's handling of historical linguistic peculiarities such as phonetic loans, polysemy, and syntactic inversions. We further integrated these tasks into the PhiloBenchmark, establishing a new standard for evaluating ancient Chinese LLMs addressing philology tasks. Deploying PhiloGPT in practical scenarios has enabled Dunhuang specialists to resolve philology tasks, such as identifying duplication of copied text and assisting archaeologists with text completion, demonstrating its potential in real-world applications. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing language Model with Unit Test Techniques for Efficient Regular Expression Generation

Enhancing Language Model with Unit Test Techniques for Effic...

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2023 conference on empirical methods in natural language processing, EMNLP 2023

作者： Mao, Chenhui Lin, Xiexiong Jin, Xin Zhang, Xin Ant Group China

Recent research has investigated the use of generative language models to produce regular expressions with semantic-based approaches. However, these approaches have shown shortcomings in practical applications, particularly in terms of functional correctness, which refers to the ability to reproduce the intended function inputs by the user. To address this issue, we present a novel method called Unit-Test Driven Reinforcement Learning (UTD-RL). Our approach differs from previous methods by taking into account the crucial aspect of functional correctness and transforming it into a differentiable gradient feedback using policy gradient techniques. In which functional correctness can be evaluated through Unit Test, a testing method that ensures regular expressions meets its design and performs as intended. Experiments conducted on public datasets demonstrate the effectiveness of the proposed method in generating regular expressions. This method has been employed in a regulatory scenario where regular expressions can be utilized to ensure that all online content is free from non-compliant elements, thereby significantly reducing the workload of relevant personnel. ©2023 Association for Computational Linguistics.

关键词： Semantics

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International conference Recent Advances in natural language processing, RANLP 2021: Deep Learning for natural language processing methods and Applications - Proceedings

International Conference Recent Advances in Natural Language...

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International conference on Recent Advances in natural language processing: Deep Learning for natural language processing methods and Applications, RANLP 2021

ISBN: (纸本)9789544520724

The proceedings contain 185 papers. The topics discussed include: ontology population reusing resources for dialogue intent detection: generic and multilingual approach;efficient multilingual text classification for Indian languages;domain adaptation for Hindi-Telugu machine translation using domain specific back translation;towards a better understanding of noise in natural language processing;comparing supervised machine learning techniques for genre analysis in software engineering research articles;enriching the transformer with linguistic factors for low-resource machine translation;interactive learning approach for Arabic target-based sentiment analysis;probabilistic ensembles of zero- and few-shot learning models for emotion classification;and predicting the factuality of reporting of news media using observations about user attention in their YouTube channels.

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Large language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark

Large Language Models Are Poor Clinical Decision-Makers: A C...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Liu, Fenglin Li, Zheng Zhou, Hongjian Yin, Qingyu Yang, Jingfeng Tang, Xianfeng Luo, Chen Zeng, Ming Jiang, Haoming Gao, Yifan Nigam, Priyanka Nag, Sreyashi Yin, Bing Hua, Yining Zhou, Xuan Rohanian, Omid Thakur, Anshul Clifton, Lei Clifton, David A. Institute of Biomedical Engineering Department of Engineering Science University of Oxford United Kingdom Amazon United States Harvard T.H. Chan School of Public Health United States Institut polytechnique de Paris France Nuffield Department of Population Health University of Oxford United Kingdom Oxford-Suzhou Centre for Advanced Research Suzhou China

ISBN: (纸本)9798891761643

The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with answer options for evaluation. However, many clinical decisions involve answering open-ended questions without pre-set options. To better understand LLMs in the clinic, we construct a benchmark ClinicBench. We first collect eleven existing datasets covering diverse clinical language generation, understanding, and reasoning tasks. Furthermore, we construct six novel datasets and clinical tasks that are complex but common in real-world practice, e.g., open-ended decision-making, long document processing, and emerging drug analysis. We conduct an extensive evaluation of twenty-two LLMs under both zero-shot and few-shot settings. Finally, we invite medical experts to evaluate the clinical usefulness of LLMs. © 2024 Association for Computational Linguistics.

关键词： Question answering

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