News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension indep...News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.展开更多
Evidential Document-level Event Factuality Identification(EvDEFI)aims to predict the factual nature of an event and extract evidential sentences from the document precisely.Previous work usually limited to only predic...Evidential Document-level Event Factuality Identification(EvDEFI)aims to predict the factual nature of an event and extract evidential sentences from the document precisely.Previous work usually limited to only predicting the factuality of an event with respect to a document,and neglected the interpretability of the task.As a more fine-grained and interpretable task,EvDEFI is still in the early stage.The existing model only used shallow similarity calculation to extract evidences,and employed simple attentions without lexical features,which is quite coarse-grained.Therefore,we propose a novel EvDEFI model named Heterogeneous and Extractive Graph Attention Network(HEGAT),which can update representations of events and sentences by multi-view graph attentions based on tokens and various lexical features from both local and global levels.Experiments on EB-DEF-v2 corpus demonstrate that HEGAT model is superior to several competitive baselines and can validate the interpretability of the task.展开更多
This paper focuses on document-level event factuality identification (DEFI), which predicts the factual nature of an event from the view of a document. As the document-level sub-task of event factuality identification...This paper focuses on document-level event factuality identification (DEFI), which predicts the factual nature of an event from the view of a document. As the document-level sub-task of event factuality identification (EFI), DEFI is a challenging and fundamental task in natural language processing (NLP). Currently, most existing studies focus on sentence-level event factuality identification (SEFI). However, DEFI is still in the early stage and related studies are quite limited. Previous work is heavily dependent on various NLP tools and annotated information, e.g., dependency trees, event triggers, speculative and negative cues, and does not consider filtering irrelevant and noisy texts that can lead to wrong results. To address these issues, this paper proposes a reinforced multi-granularity hierarchical network model: Reinforced Semantic Learning Network (RSLN), which means it can learn semantics from sentences and tokens at various levels of granularity and hierarchy. Since integrated with hierarchical reinforcement learning (HRL), the RSLN model is able to select relevant and meaningful sentences and tokens. Then, RSLN encodes the event and document according to these selected texts. To evaluate our model, based on the DLEF (Document-Level Event Factuality) corpus, we annotate the ExDLEF corpus as the benchmark dataset. Experimental results show that the RSLN model outperforms several state-of-the-arts.展开更多
Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it shoul...Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.展开更多
The increasing demand for unconventional oil and gas resources,especially oil shale,has highlighted the urgent need to develop rapid and accurate strata characterization methods.This paper is the first case and examin...The increasing demand for unconventional oil and gas resources,especially oil shale,has highlighted the urgent need to develop rapid and accurate strata characterization methods.This paper is the first case and examines the drilling process monitoring(DPM)method as a digital,accurate,cost-effective method to characterize oil shale reservoirs in the Ordos Basin,China.The digital DPM method provides real-time in situ testing of the relative variation in rock mechanical strength along the drill bit depth.Furthermore,it can give a refined rock quality designation based on the DPM zoning result(RQD(V_(DPM)))and a strength-grade characterization at the site.Oil shale has high heterogeneity and low strata strength.The digital results are further compared and verified with manual logging,cored samples,and digital panoramic borehole cameras.The findings highlight the innovative potential of the DPM method in identifying the zones of oil shale reservoir along the drill bit depth.The digital results provide a better understanding of the oil shale in Tongchuan and the potential for future oil shale exploration in other regions.展开更多
基金funded by“the Fundamental Research Funds for the Central Universities”,No.CUC23ZDTJ005.
文摘News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.62006167 and 62276177)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Evidential Document-level Event Factuality Identification(EvDEFI)aims to predict the factual nature of an event and extract evidential sentences from the document precisely.Previous work usually limited to only predicting the factuality of an event with respect to a document,and neglected the interpretability of the task.As a more fine-grained and interpretable task,EvDEFI is still in the early stage.The existing model only used shallow similarity calculation to extract evidences,and employed simple attentions without lexical features,which is quite coarse-grained.Therefore,we propose a novel EvDEFI model named Heterogeneous and Extractive Graph Attention Network(HEGAT),which can update representations of events and sentences by multi-view graph attentions based on tokens and various lexical features from both local and global levels.Experiments on EB-DEF-v2 corpus demonstrate that HEGAT model is superior to several competitive baselines and can validate the interpretability of the task.
基金supported by the National Natural Science Foundation of China under Grant Nos.62006167,62276177,62376181,and 62376178the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No.24KJB520036the Project Funded by the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.
文摘This paper focuses on document-level event factuality identification (DEFI), which predicts the factual nature of an event from the view of a document. As the document-level sub-task of event factuality identification (EFI), DEFI is a challenging and fundamental task in natural language processing (NLP). Currently, most existing studies focus on sentence-level event factuality identification (SEFI). However, DEFI is still in the early stage and related studies are quite limited. Previous work is heavily dependent on various NLP tools and annotated information, e.g., dependency trees, event triggers, speculative and negative cues, and does not consider filtering irrelevant and noisy texts that can lead to wrong results. To address these issues, this paper proposes a reinforced multi-granularity hierarchical network model: Reinforced Semantic Learning Network (RSLN), which means it can learn semantics from sentences and tokens at various levels of granularity and hierarchy. Since integrated with hierarchical reinforcement learning (HRL), the RSLN model is able to select relevant and meaningful sentences and tokens. Then, RSLN encodes the event and document according to these selected texts. To evaluate our model, based on the DLEF (Document-Level Event Factuality) corpus, we annotate the ExDLEF corpus as the benchmark dataset. Experimental results show that the RSLN model outperforms several state-of-the-arts.
文摘Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.
基金supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region,China(Grant No.HKU 7137/03E)the National Natural Science Foundation of China(Grant No.41977248)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB10030100).
文摘The increasing demand for unconventional oil and gas resources,especially oil shale,has highlighted the urgent need to develop rapid and accurate strata characterization methods.This paper is the first case and examines the drilling process monitoring(DPM)method as a digital,accurate,cost-effective method to characterize oil shale reservoirs in the Ordos Basin,China.The digital DPM method provides real-time in situ testing of the relative variation in rock mechanical strength along the drill bit depth.Furthermore,it can give a refined rock quality designation based on the DPM zoning result(RQD(V_(DPM)))and a strength-grade characterization at the site.Oil shale has high heterogeneity and low strata strength.The digital results are further compared and verified with manual logging,cored samples,and digital panoramic borehole cameras.The findings highlight the innovative potential of the DPM method in identifying the zones of oil shale reservoir along the drill bit depth.The digital results provide a better understanding of the oil shale in Tongchuan and the potential for future oil shale exploration in other regions.
