Detecting Narrative Elements in Informational Text
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）
Effi Levi, Guy Mor, Tamir Sheafer, Shaul R. Shenhav
备注：
Accepted to Finding of NAACL 2022. Dataset is available at https://github.com/efle/NEAT. arXiv admin note: substantial text overlap with arXiv:2007.04874
Automatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) - a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains. We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.
Identifying Informational Sources in News Articles
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of California, Los Angeles（加州大学洛杉矶分校）；Annenberg School of Communication, University of Southern California（南加州大学安纳伯格传播学院）
Detecting Narrative Shifts through Persistent Structures: A Topological Analysis of Media Discourse
Department of Cyber Intelligence and Data Science（网络智能与数据科学系）；Biological and Computational Intelligence Center（生物与计算智能中心）；National Intelligence University（国家智能大学）
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media
Centro Singular de Investigación en Tecnoloxías Intelixentes da USC（乌拉特大学智能技术研究中心）；IRLab, CITIC Research Centre, Universidade da Coruña（IR实验室，CITIC研究中心，科鲁纳大学）；Universidade de Évora（埃夫拉大学）；Universidad de La Rioja（里瓦达维亚大学）；GESIS Leibniz Institute for the Social Sciences（莱比锡社会科学院）
Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Karolinska Institutet（卡罗林斯卡学院）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
University of Sydney（悉尼大学）；Success Beyond Pain（超越痛苦（机构名直译））；Hello Sunday Morning（你好周日早晨（机构名直译））；Imperial College London（帝国理工学院）；University of New South Wales（新南威尔士大学）
In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability. Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal anno…
NLP-based detection of systematic anomalies among the narratives of consumer complaints
School of Mathematical and Statistical Sciences, Western University（西安大略大学数学与统计学院）；Agri-Food Analytics Lab, Dalhousie University（达尔豪斯大学农业食品分析实验室）；Canada Life（加拿大永明金融）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院医疗服务科学研究所）
École de Technologie Supérieure, University of Québec（魁北克大学高等技术学院）；CHU Sainte-Justine Hospital, University of Montreal（蒙特利尔大学圣茹斯特医院）
The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation…
Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（佛罗里达大学医学院健康结果与生物医学信息学系）；Department of Epidemiology, College of Public Health and Health Professions, University of Florida（佛罗里达大学公共卫生与健康专业学院流行病学系）；Preston A. Wells, Jr. Center for Brain Tumor Therapy, Lillian S. Wells Department of Neurosurgery, University of Florida（佛罗里达大学莉莉安·S·威尔斯神经外科系普雷斯顿·A·威尔斯脑肿瘤治疗中心）
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用科学大学）；University of Trento（特伦托大学）
Personal narratives (PN) - spoken or written - are recollections of facts, people, events, and thoughts from one's own experience. Emotion recognition and sentiment analysis tasks are usually defined at the utterance or document level. However, in this work, we focus on Emotion Carriers (EC) defined as the segments (speech or text) that best explain the emotional state of the narrator ("loss of father", "made me choose"). Once extracted, such EC can provide a richer representation of the user state to improve natural language understanding and dialogue modeling. In previous work, it has been s…
Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that featurebased and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their "bias predictiveness" is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection…
Vaccine hesitancy, which has recently been driven by online narratives, significantly degrades the efficacy of vaccination strategies, such as those for COVID-19. Despite broad agreement in the medical community about the safety and efficacy of available vaccines, a large number of social media users continue to be inundated with false information about vaccines and are indecisive or unwilling to be vaccinated. The goal of this study is to better understand anti-vaccine sentiment by developing a system capable of automatically identifying the users responsible for spreading anti-vaccine narrat…
Text characterization based on recurrence networks
Institute of Mathematics and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学学院）；Indiana University Network Science Institute（印第安纳大学网络科学研究所）；São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理研究所）；ISI Foundation（ISI基金会）
Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles
University of Dhaka（达卡大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Cisco Systems（思科系统）；BanglaLLM；Maharishi International University（玛哈里希国际大学）；Unityflow AI
Since fake news poses a serious threat to society and individuals, numerous studies have been brought by considering text, propagation and user profiles. Due to the data collection problem, these methods based on propagation and user profiles are less applicable in the early stages. A good alternative method is to detect news based on text as soon as they are released, and a lot of text-based methods were proposed, which usually utilized words, sentences or paragraphs as basic units. But, word is a too fine-grained unit to express coherent information well, sentence or paragraph is too coarse …
