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Deep Semantic Understanding
深度语义理解
Social Computing
社会计算
AI Engineering
人工智能工程
学术前沿

Extracting Impacts of Non-pharmacological Interventions for COVID-19 From Modelling Study

COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity,non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 abstracts of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government.

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Identifying Risks of the Internet Finance Platforms Using Multi-Source Text Data

With the explosion of the Internet Finance Platforms, identifying the risks of these platforms is of growing significance, which can help discover problematic platforms in time and ensure the healthy development of the Internet finance industry. In this paper, we design a risk index system to measure the quantitative risk of the Internet finance platforms, and propose a deep neural network based model, CBiGRU-RI, to identify the risks of the platforms using multi-source text data. We conducted comparative experiments with various baseline models on real-world data. The experimental results show that our proposed model can identify the risks of platforms more effectively than the baseline methods.

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A Framework for Policy Information Popularity Prediction in New Media

With the rapid development and wide application of new media, predicting the popularity of policyinformation on new media is of great significance for understanding and managing public opinion. However, the complexity of the diffusion patterns of policy information has brought great challenges for predicting the popularity of such information. Inspired by the methods of popularity prediction for short text information from social networks, we propose a framework for the popularity prediction of policy information. In our framework, first, the features of policy information are extracted from three dimensions: contextual information, social information and textual information. Then, effective features, such as the topic distribution, popularity competition intensity and hot information relevance, are identified by empirical analysis. Finally, the effective features are input into the prediction model to predict the popularity of policy information. We evaluate the performance of our proposed framework using a real-world dataset and the experimental results show that the framework can efficiently predict the popularity of policy information and that the features that we used are effective in improving the accuracy of policy information popularity prediction. The accurate prediction result could benefit policy makers, allowing them to make better decisions, understand and manage public opinion.

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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field.

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Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our
SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

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A Central Opinion Extraction Framework for Boosting Performance on Sentiment Analysis

With the rapid development of the Internet, mining opinions and emotions from the explosive growth of user generated content is a key field of social media analysis. However, the expression forms of the central opinion which strongly expresses the essential points and converges the main sentiments of the overall document are diverse in practice, such as sequential sentences, a sentence fragment, or an individual sentence. Previous research studies on sentiment analysis based on document level and sentence level fail to deal with this actual situation uniformly. To address this issue, we propose a Central Opinion Extraction (COE) framework to boost performance on sentiment analysis with social media texts. Our framework first extracts a span-level central opinion text, which expresses the essential opinion related to sentiment representation among the whole text, and then uses extracted textual span to boost the performance of sentiment classifiers. The experimental results on a public dataset show the effectiveness of our framework for boosting the performance on document-level sentiment analysis task.

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A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media

The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity o... View more

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基于高斯混合模型的无线传感器网络定位算法

针对距离误差对定位结果的影响,提出一种基于高斯混合模型的无线传感器网络定位算法.该算法将高斯混合模型方法引入到无线传感器网络的定位问题中,通过高斯混合模型分析找出误差较大的距离信息并将其剔除,对剩余距离信息使用三边测量定位法进行定位求解,同时结合加权定位算法进行位置估计.仿真实验结果表明,改进算法能提高定位精度,且定位结果更稳定。

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Integrating Deep Learning Approaches for Identifying News Reprint Relation

With the rapid development of big data and new media technologies, a large amount of original news is generated and reprinted on the Internet via news portals. Identifying news reprint relations is of great importance for the analysis of news diffusion patterns and copyright protection. However, the amount of news data on the Internet creates a huge challenge for efficiently identifying news reprint relation. Some existing studies focus on computing the similarity of the full text of news reports, which is not always effective, because some reprints only excerpt some sentences of the original news reports. The core challenge of improving identification accuracy is excavating the potential semantic relevance between news articles at the sentence level. Inspired by deep learning and semantic-based text representation models, this paper proposes an approach for identifying news reprint relation by integrating deep learning approaches. First, news reports that are not related to the topic of the original news report are removed via topic correlation mining. Then, the potential semantic relevance is excavated at the sentence level through the integration of semantic analysis methods, and reprint relations are identified between news reports. The performance of the approach is empirically evaluated using a real-world dataset. Experimental results show that the semantic analysis model integration allows us to mine in-depth semantic associations between news stories and accurately identify news reprint relations. These results benefit news diffusion pattern analysis and copyright protection.

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Exploring Trends and Patterns of Popularity Stage Evolution in Social Media

以往针对互联网事件传播分析和预测工作中往往只对其发展趋势进行刻画，而缺少在更细粒度上对其发展演化阶段的建模和预测。本研究工作综合考虑了参与用户和事件自身在内容、结构和关联关系等多方面的动态影响因素，并同时挖掘其演化模式信息，提出一种融合动态因素和演化模式的事件发展阶段预测方法。

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Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective

本文借鉴认知心理学领域中的ACT记忆激活理论来实现文本隐含语义的分析与语义增强表示。文章首先从信息论的角度论证该理论在文本表示中的应用原理与可行性，并提出基于关联激活的文本语义增强表示方法。

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SRGCN: Graph-based multi-hop reasoning on knowledge graphs

本文提出了序列关系图卷积网络，通过建模语义信息在知识图谱上的传递，来推理图谱中缺失的链接。不同于现有方法通过路径序列传递信息的方式，本文将图结构作为信息传递的基本单位，令信息通过子图传递，从而更好地编码图结构信息与不同路径间的交互作用。

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Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction

本文受人类语言理解中类量子现象的启发，通过结合量子概率和图注意力机制，提出一种量子概率启发图注意力网络，将量子框架从建模单词序列泛化到建模复杂文本交互。该网络将每个文本节点建模为处于叠加态的粒子，并将图中每个节点的邻域建模为混合态，以学习交互增强的文本节点表示。

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