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Multi Layered rule-based Technique for Explicit Aspect Extraction from Online Reviews

Computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.Usman Ashraf Muhammad Waseem Iqbal Ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key task.Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature.rule based approaches,like dependency-based rules,are quite popular and effective for this purpose.However,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency parser.Another popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s behavior.However,in general,the sequential rule-based approaches have poor generalization capability.Moreover,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb aspects.In this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects.Additionally,after rigorous analysis,we have also constructed rules for the extraction of verb aspects.These verb rules primarily based on the association between verb and opinion words.The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets.The F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing approaches.These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

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Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy rule-based Inference Systems

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Computer Modeling in Engineering & Sciences 2017年第4期113卷 443-459页

作者： Sunil Kr.Jha Zulfiqar Ahmad School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Environmental Sciences University of CaliforniaRiversideCA 92521USA

Microbial population and enzyme activities are the significant indicators of soil strength.Soil microbial dynamics characterize microbial population and enzyme activities.The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase activity.More specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics.Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected strains.Three experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and WM-FIS.The better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.

关键词： Phosphate solubilizing bacteria bacterial population ACC-deaminase activity subtractive clustering fuzzy rule-based prediction system

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Representing the Knowledge of Public Construction Project Cost Estimator by Using rule-based Method

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Journal of Building Construction and Planning Research 2015年第4期3卷 189-195页

作者： Abelrahman Osman Elfaki Saleh Alatawi University of Tabuk Tabuk KSA Binladen Research Chair of Quality and Productivity Improvement in the Construction Industry College of Engineering University of Hail Hail KSA

Despite the presence of various construction project cost estimate softwares, human experience and knowledge cannot be disregarded. This fact has been proven in practice, where the success of construction cost estimate process is mainly based on knowledge of human estimator. The main question concerns what human knowledge determines the success of the construction cost estimation process. To address this question we have applied Delphi technique and the output is eleven factors that are enough to precisely represent construction cost estimator knowledge. Then we have used First Order Logic (FOL) to represent these factors in terms of predicates and rules. These FOL rules could be used for evaluating construction cost estimator knowledge in five classes: fail, pass, acceptable, good, and very good. As a validation process we have done experiments using history data and the results have proved the accuracy of our proposed method.

关键词： Construction Management Construction Cost Estimation rule-based System

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Applying Fuzzy rule-based System on FMEA to Assess the Risks on Project-based Software Engineering Education

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Journal of Software Engineering and Applications 2017年第7期10卷 591-604页

作者： Issarapong Khuankrue Fumihiro Kumeno Yutaro Ohashi Yasuhiro Tsujimura Electronics Information and Media Engineering Major Graduate School Nippon Institute of Technology Saitama Japan Department of Computer and Information Engineering Nippon Institute of Technology Saitama Japan

Project-based learning has been in widespread use in education. However, project managers are unaware of the students’ lack of experience and treat them as if they were professional staff. This paper proposes the application of a fuzzy failure mode and effects analysis model for project-based software engineering education. This method integrates the fuzzy rule-based system with learning agents. The agents construct the membership function from historical data. Data are processed by a clustering process that facilitates the construction of the membership function. It helps students who lack experience in risk assessment to develop their expertise in that skill. The paper also suggests a classification technique for a fuzzy rule-based system that can be used to judge risk based on a fuzzy inference system. The student project will thus be further enhanced with respect to risk assessment. We then discuss the design of experiments to verify the proposed model.

关键词： Risk Assessment Project-based Learning Failure Mode and Effects Analysis Fuzzy rule-based System Intelligent Agents

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Establishing a rule-based International Order is a Difficult and Long Process

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Peace 2019年第3期 75-78页

作者： Yu Hongjun the CPAPD IDCPC

In recent years,the complex and changeable world situation has become particularly prominent,and the trend of international relations,especially the major country relations is uncertain,which is of great concern.The establishment of a new international order based on new rules is becoming the focus and gravity of study of international relations.

关键词： Establishing a rule-based International Order a Difficult and Long Process

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Applying DNA Computation to Error Detection Problem in rule-based Systems

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Journal of Intelligent Learning Systems and Applications 2015年第1期7卷 21-36页

作者： Behrouz Madahian Amin Salighehdar Reza Amini Department of Mathematical Sciences University of Memphis Memphis USA Financial Engineering Department Stevens Institute of Technology Hoboken USA Department of Computer Science Florida International University Miami USA

As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses;q is the number of rules in the longest inference chain;K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes;and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.

关键词： DNA Computing rule-based Systems rule Verification Structural Errors

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Building Case-based Preliminary Design Systems: A Hopfield Network Approach

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Journal of Computer Science & Technology 1994年第4期9卷 329-341页

作者：吴维钟万勰盛之进 ResearchInstituteofEngineeringMechanics DalianUniversityofTechnologyDalian116024

This paPer addresses the issue of building a case-based preliminary design system by using Hopfield networks. one limitation of Hopfield networks is that it cannot be tralned, i.e. the weights between two neurons must be set in advance. A pattern stored in Hopfield networks cannot be recalled if the pattern is not a local minimum.Two concepts are proposed to deal with this problem. They are the multiple training encoding method and the puppet encoding method. The multiple training encoding method, which guarantees to recall a single stored pattern under appropriate initial conditions of data, is theoretica-lly analyzed, and the minimal number of times for using a pattern for training to guarantee recalling of the pattern among a set of patterns is derived. The puppet encoding method is proved to be able to guarantee recalling of all stored patterns if attaching puppet data to the stored patterns is available.An integrated software PDS (Prelindnary Design System), which is developed from two aspects, is described. One is from a case-based expert system-CPDS (Case-based Prelindnary Design System), which is based on the algorithm of the Hopfield and developed for uncertain problems in PDS; the other is RPDS (rule-based Preliminary Design System), which attacks logic or deduced problems in PDS. based on the results of CPDS, RPDS can search for feasible solutioll in design model. CPDS is demonstrated to be useful in the domains of preliminary designs of cable-stayed bridges in this paper.

关键词： Hopfield multiple training encoding method puppet encoding method,cases-based rule-based

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Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery

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World Journal of Clinical Oncology 2018年第5期9卷 98-109页

作者： Jeya Balaji Balasubramanian Vanathi Gopalakrishnan Intelligent Systems Program School of Computing and Information University of Pittsburgh Department of Biomedical Informatics School of Medicine University of Pittsburgh

AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model

关键词： Supervised machine learning rule-based models Bayesian methods Background knowledge Informative priors Biomarker discovery

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Sentiment Analysis of Japanese Tourism Online Reviews

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Journal of Data and Information Science 2019年第1期4卷 89-113页

作者： Chuanming Yu Xingyu Zhu Bolin Feng Lin Cai Lu An School of Information and Safety Engineering Zhongnan University of Economics and Law School of Information Management Wuhan University

Purpose: Online reviews on tourism attractions provide important references for potential tourists to choose tourism spots. The main goal of this study is conducting sentiment analysis to facilitate users comprehending the large scale of the reviews, based on the comments about Chinese attractions from Japanese tourism website 4 Travel.Design/methodology/approach: Different statistics-and rule-based methods are used to analyze the sentiment of the reviews. Three groups of novel statistics-based methods combining feature selection functions and the traditional term frequency-inverse document frequency(TF-IDF) method are proposed. We also make seven groups of different rulesbased methods. The macro-average and micro-average values for the best classification results of the methods are calculated respectively and the performance of the methods are shown.Findings: We compare the statistics-based and rule-based methods separately and compare the overall performance of the two method. According to the results, it is concluded that the combination of feature selection functions and weightings can strongly improve the overall performance. The emotional vocabulary in the field of tourism(EVT), kaomojis, negative and transitional words can notably improve the performance in all of three categories. The rule-based methods outperform the statistics-based ones with a narrow advantage.Research limitation: Two limitations can be addressed: 1) the empirical studies to verify the validity of the proposed methods are only conducted on Japanese languages; and 2) the deep learning technology is not been incorporated in the methods.Practical implications: The results help to elucidate the intrinsic characteristics of the Japanese language and the influence on sentiment analysis. These findings also provide practical usage guidelines within the field of sentiment analysis of Japanese online tourism reviews.Originality/value: Our research is of practicability. Currently, there are no studies that focus on the sentiment analysis of Japa

关键词： Sentiment analysis Japanese reviews rule-based methods Statistics-based methods Tourism reviews

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AN INTELLIGENT MACHINE TRANSLATION rule BASE SYSTEM IMT-RB/EC

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Science China Mathematics 1990年第8期33卷 1016-1024页

作者：叶一民陈肇雄张祥高庆狮 Institute of Computing Technology Academia Sinica Beijing 100080 PRC

The knowledge reorganization is of great importance in a knowledge base system. A concept and a method for multi-reorganizing rule-based systems using characteristics (attributes) of rules are presented, and an intelligent machine translation rule base system IMT-RB/EC based on this method is introduced. The system is, in essence, a knowledge base system with learning and self-reorganization capacities, and it has a clear structure with features of representing, assorting, refining, searching, improving and expanding knowledge.

关键词： rule-based system knowledge group knowledge attribute multi-reorganization.

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