scholarly journals A Discourse Coherence Analysis Method Combining Sentence Embedding and Dimension Grid

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lanlan Jiang ◽  
Shengjun Yuan ◽  
Jun Li
Keyword(s):  
Feature Vector ◽  
Transition Probabilities ◽  
Coherence Analysis ◽  
Analysis Method ◽  
Excellent Performance ◽  
Language Generation ◽  
Fine Grained ◽  
Discourse Coherence ◽  
Text Quality ◽  
Sentence Level

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. However, existing coherence models focus on measuring individual aspects of coherence, such as lexical overlap, entity centralization, rhetorical structure, etc., lacking measurement of the semantics of text. In this paper, we propose a discourse coherence analysis method combining sentence embedding and the dimension grid, we obtain sentence-level vector representation by deep learning, and we introduce a coherence model that captures the fine-grained semantic transitions in text. Our work is based on the hypothesis that each dimension in the embedding vector is exactly assigned a stated certainty and specific semantic. We take every dimension as an equal grid and compute its transition probabilities. The document feature vector is also enriched to model the coherence. Finally, the experimental results demonstrate that our method achieves excellent performance on two coherence-related tasks.

scholarly journals Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Meng Zhang ◽  
Min Kang ◽  
Ai-Ping Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Feature Vector ◽  
Urban Environments ◽  
Environmental Feature ◽  
Fine Grained ◽  
Environmental Features ◽  
Street View ◽  
The City ◽  
Dengue Epidemic

Abstract Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

scholarly journals Predicting and Analyzing Language Specificity in Social Media Posts

2019 ◽  
Vol 33 ◽  
pp. 6415-6422 ◽  
Author(s):  
Yifan Gao ◽  
Yang Zhong ◽  
Daniel Preoţiuc-Pietro ◽  
Junyi Jessy Li
Keyword(s):  
Social Media ◽  
Computational Linguistics ◽  
Large Scale ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Fine Grained ◽  
Argumentation Mining ◽  
Sentence Level ◽  
Prediction Systems ◽  
Mental Health Factors

In computational linguistics, specificity quantifies how much detail is engaged in text. It is an important characteristic of speaker intention and language style, and is useful in NLP applications such as summarization and argumentation mining. Yet to date, expert-annotated data for sentence-level specificity are scarce and confined to the news genre. In addition, systems that predict sentence specificity are classifiers trained to produce binary labels (general or specific).We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale. Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We also present the first large-scale study revealing the social, temporal and mental health factors underlying language specificity on social media.

Exponential stability for Markovian neutral stochastic systems with general transition probabilities and time-varying delay

2018 ◽  
Vol 41 (2) ◽  
pp. 350-365 ◽  
Author(s):  
Xin Zhang ◽  
Huashan Liu ◽  
Yiyuan Zheng ◽  
Yuqing Sun ◽  
Wuneng Zhou ◽  
...  
Keyword(s):  
Exponential Stability ◽  
Lyapunov Functions ◽  
Stochastic Systems ◽  
Transition Probabilities ◽  
Time Varying ◽  
Analysis Method ◽  
Multiple Lyapunov Functions ◽  
Time Varying Delay ◽  
Neutral Stochastic Systems ◽  
Varying Delay

This paper discusses the problem of exponential stability for Markovian neutral stochastic systems with general transition probabilities and time-varying delay. Based on non-convolution type multiple Lyapunov functions and stochastic analysis method, we obtain the conditions which are independent to any decay rate of the exponential stability for uncertain transition probabilities neutral stochastic systems with time-varying delay. Finally, two examples are presented to illustrate the effectiveness and potential of the proposed results.

scholarly journals Research on Fine-Grained Classification of Rumors in Public Crisis ——Take the COVID-19 incident as an example

2020 ◽  
Vol 179 ◽  
pp. 02027
Author(s):  
Shuaipu Chen
Keyword(s):  
Reference Value ◽  
Training Model ◽  
Fine Tuning ◽  
The Public ◽  
Fine Grained ◽  
Model Method ◽  
Public Crisis ◽  
Sentence Level ◽  
Good Classification

[Purpose / Meaning] Rumors are frequent in the COVID-19 epidemic crisis. In order to unite the power of dispelling rumors of various media platforms to help to break the rumors in a timely and professional manner, this article has designed a new fine-grained classification of rumors about COVID-19 based on the BERT model. [Method / Process] Based on the rumor data of several mainstream rumor refuting platforms, the pre-training model of BERT was used to fine-tuning in the context of COVID-19 events to obtain the feature vector representation of the rumor sentence level to achieve fine-grained classification, and a comparative experiment was conducted with the TextCNN and TextRNN models. [Result / Conclusion] The results show that the classificationF1 value of the model designed in this paper reaches 98.34%, which is higher than the TextCNN and TextRNN models by 2%, indicating that the model in this paper has a good classification judgment ability for COVID-19 rumors, and provides certain reference value for promoting the coordinated refuting of rumors during the public crisis.

scholarly journals A Statistical, Grammar-Based Approach to Microplanning

2017 ◽  
Vol 43 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Claire Gardent ◽  
Laura Perez-Beltrachini
Keyword(s):  
Statistical Approach ◽  
Hybrid Approach ◽  
Human Study ◽  
Knowledge Bases ◽  
Symbolic System ◽  
Language Generation ◽  
Fine Grained ◽  
Sentence Segmentation ◽  
Symbolic Approach ◽  
Surface Realization

Although there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine-grained interactions that arise during microplanning between aggregation, surface realization, and sentence segmentation. In this article, we propose a hybrid symbolic/statistical approach to jointly model the constraints regulating these interactions. Our approach integrates a small handwritten grammar, a statistical hypertagger, and a surface realization algorithm. It is applied to the verbalization of knowledge base queries and tested on 13 knowledge bases to demonstrate domain independence. We evaluate our approach in several ways. A quantitative analysis shows that the hybrid approach outperforms a purely symbolic approach in terms of both speed and coverage. Results from a human study indicate that users find the output of this hybrid statistic/symbolic system more fluent than both a template-based and a purely symbolic grammar-based approach. Finally, we illustrate by means of examples that our approach can account for various factors impacting aggregation, sentence segmentation, and surface realization.

scholarly journals Analysis of Company Financial Performance Perum Jasa Tirta

2020 ◽  
Vol 3 (3) ◽  
pp. 25-31
Author(s):  
Yuli Agustina ◽  
Wita Ryani Juniar ◽  
Heri Pratikto ◽  
Ely Siswanto
Keyword(s):  
Financial Performance ◽  
Financial Statement ◽  
Financial Ratios ◽  
Analysis Method ◽  
Excellent Performance ◽  
Financial Statement Analysis ◽  
Poor Condition ◽  
Activity Ratios ◽  
Profitability Ratios

The purpose of this study is to determine the financial performance of Perum Jasa Tirta I Malang-East Java for the period 2009-2018. This type of research used in this study is a description and the method used to measure financial performance is one of the methods of financial statement analysis. The financial statement analysis method involves several financial ratios namely liquidity ratios, leverage, activity and profitability and is measured based quantitatively data. The results showed that: Information on financial performance is needed in maintaining the company's existence; this is evident from the results Financial performance of Perum Jasa Tirta I, as seen from profitability ratios and leverage ratios, showed excellent performance even though profitability ratios and activity ratios are known to be in poor condition.

scholarly journals Users’ Sentiment Analysis of Shopping Websites Based on Online Reviews

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xiaohong Wang ◽  
Shuang Dong
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
Online Shopping ◽  
Rapid Development ◽  
Online Reviews ◽  
Analysis Method ◽  
Purchasing Decisions ◽  
Emotional Words ◽  
Fine Grained ◽  
The Relationship

AbstractWith the rapid development of online shopping, how to explore the value of online reviews, so as to give full play to their role in potential users’ purchasing decisions. Based on text mining and quantitative analysis, this paper studies the sentiment analysis of online reviews on B2C shopping website. The main attributes of commodity or service are extracted based on the order of word frequency in the online reviews. Text analysis method is used to judge the relationship between attributes of commodity or service and its emotional words. The fine-grained sentimental polarity and intensity of attributes are identified to analyze users’ concerns and preferences. The research shows that users pay more attention to the configuration and after-sales service of mobile, and have a positive sentimental orientation to most of attributes, especially unlocking function, hand feeling attribute and logistics service; and have a neutral sentimental orientation towards the attributes of battery and memory, and a negative sentimental orientation towards the membrane of mobile phone. The results can provide a reference for consumers to make purchasing decisions, for enterprises to improve product quality, and for shopping platform to optimize service.

New Calorimeter Techniques

2018 ◽  
Author(s):  
Richard Wigmans
Keyword(s):  
Tracking System ◽  
Flow Analysis ◽  
Simultaneous Detection ◽  
Analysis Method ◽  
Fine Grained ◽  
Design Flexibility ◽  
Shower Development ◽  
Gaussian Line Shape ◽  
Compensating Devices ◽  
Gaussian Line

This chapter is dedicated to calorimeter techniques that have been developed since the first edition of this monograph was published (2000). The Dual Readout Method (DREAM) aims to combine the advantages of compensation (linearity, excellent hadron resolution, Gaussian line shape) with a certain amount of design flexibility. This method, based on simultaneous detection of scintillation and Cherenkov light produyced in the shower development, eliminates some of the disadvantages of compensating devices, and in particular the dependence on efficient neutron detection of the latter. The Particle Flow Analysis method aims to combine the information provided by a good tracking system with that provided by a fine-grained calorimeter system to obtain excellent performance for the detection of jets. The results achieved with both methods, and the challenges faced in practice, are described in detail.

scholarly journals BAHK: Flexible Automated Binary Analysis Method with the Assistance of Hardware and System Kernel

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Jiaye Pan ◽  
Yi Zhuang ◽  
Binglin Sun
Keyword(s):  
Just In Time ◽  
Analysis Method ◽  
Memory Consumption ◽  
Binary Instrumentation ◽  
Fine Grained ◽  
Specific Analysis ◽  
Target Program ◽  
Dynamic Binary Instrumentation ◽  
The Impact ◽  
Binary Programs

To protect core functions, applications often utilize the countermeasure techniques such as antidebugging to avoid analysis by outsiders, especially the malware. Dynamic binary instrumentation is commonly used in the analysis of binary programs. However, it can be easily detected and has stability and applicability problems as it involves program rewriting and just-in-time compilation. This paper proposes a new lightweight analysis method for binary programs with the assistance of hardware features and the operating system kernel, named BAHK, which can automatically analyze the target program by stealth and has wide applicability. With the support of underlying infrastructures, this paper designs several optimization strategies and specific analysis approaches at instruction level to reduce the impact of fine-grained analysis on the performance of target program so that it can be well applied in practice. The experimental results show that the proposed method has good stealthiness, low memory consumption, and positive user experience. In some cases, it shows better analysis performance than the traditional dynamic binary instrumentation method. Finally, the real case studies further show its feasibility and effectiveness.

An Experimental Analysis of Interior Noise in an Ambulance and Noise Reduction with Damping Materials

2013 ◽  
Vol 316-317 ◽  
pp. 1118-1122
Author(s):  
Song Bai ◽  
Xin Xi Xu ◽  
Meng Yang ◽  
Xiao Hui Liu ◽  
Wei Hua Su ◽  
...  
Keyword(s):  
Structural Modification ◽  
Coherence Function ◽  
Interior Noise ◽  
Partial Coherence ◽  
Coherence Analysis ◽  
Analysis Method ◽  
Vehicle Interior ◽  
Single Output ◽  
Acoustic Contribution ◽  
Damping Materials

To solve the problem of an ambulance interior noise, a multi-input and single-output linear system model is established based on the partial coherence analysis method. In this model, vibration acceleration signals of panels are treated as input, sound pressure signals is treated as output. The relevant influence among the system inputs are ruled out and the partial coherence function value is considered as an indicator to estimate the panels’ acoustic contribution to the field point. On the basis of analysis, the structural modification with damping materials is performed on the panels with greater contribution. The results show that panels’ acoustic contribution can be analyzed by partial coherence analysis method effectively and structural modification with damping materials based on the method has significant effect on reducing the vehicle interior noise and decreasing additional mass.

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