Applying Text Classification Algorithms in Web Services Robustness Testing

2010 ◽  
Author(s):  
Nuno Laranjeiro ◽  
Rui Oliveira ◽  
Marco Vieira
Keyword(s):  
Web Services ◽  
Text Classification ◽  
Classification Algorithms ◽  
Robustness Testing

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals Analysis of Text Classification Algorithms: A Review

2019 ◽  
Vol Volume-3 (Issue-2) ◽  
pp. 579-581
Author(s):  
Nida Zafar Khan ◽  
Prof. S. R. Yadav ◽  
Keyword(s):  
Text Classification ◽  
Classification Algorithms

Adapting Test-Driven Development to Build Robust Web Services

2013 ◽  
pp. 218-237
Author(s):  
Nuno Laranjeiro ◽  
Marco Vieira
Keyword(s):  
Web Services ◽  
Software Development ◽  
Source Code ◽  
Test Cases ◽  
Services Research ◽  
Test Driven Development ◽  
Robustness Testing ◽  
Open Source Code ◽  
Testing Approach ◽  
Software Services

Web services are increasingly being used in business critical environments as a mean to provide a service or integrate distinct software services. Research indicates that, in many cases, services are deployed with robustness issues (i.e., displaying unexpected behaviors when in presence of invalid input conditions). Recently, Test-Driven Development (TDD) emerged as software development technique based on test cases that are defined before development, as a way to validate functionalities. However, programmers typically disregard the verification of limit conditions, such as the ones targeted by robustness testing. Moreover, in TDD, tests are created before developing the functionality, conflicting with the typical robustness testing approach. This chapter discusses the integration of robustness testing in TDD for improving the robustness of web services during development. The authors requested three programmers to create a set of services based on open-source code and to implement different versions of the services specified by TPC-App, using both TDD and the approach presented in this chapter. Results indicate that TDD with robustness testing is an effective way to create more robust services.

scholarly journals Fusing Logical Relationship Information of Text in Neural Network for Text Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Heyong Wang ◽  
Dehang Zeng
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Information Science ◽  
Classification Algorithms ◽  
Human Beings ◽  
Central Idea ◽  
Logical Relationship ◽  
The Relationship ◽  
Different Parts ◽  
Better Than

With the development of computer science and information science, text classification technology has been greatly developed and its application scenarios have been widened. In traditional process of text classification, the existing method will lose much logical relationship information of text. The logical relationship information of a text refers to the relationship information among different logical parts of the text, such as title, abstract, and body. When human beings are reading, they will take title as an important part to remind the central idea of the article, abstract as a brief summary of the content of the article, and body as a detailed description of the article. In most of the text classification studies, researchers concern more about the relationship among words (word frequency, semantics, etc.) and neglect the logical relationship information of text. It will lose information about the relationship among different parts (title, body, etc.) and have an influence on the performance of text classification. Therefore, we propose a text classification algorithm—fusing the logical relationship information of text in neural network (FLRIOTINN), which complements the logical relationship information into text classification algorithms. Experiments show that the effect of FLRIOTINN is better than the conventional backpropagation neural networks which does not consider the logical relationship information of text.

Robustness Testing of Web Services

2013 ◽  
pp. 55-68
Author(s):  
Aniello Napolitano ◽  
Gabriella Carrozza ◽  
Nuno Laranjeiro ◽  
Marco Vieira
Keyword(s):  
Web Services ◽  
Robustness Testing

scholarly journals Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach

Stats ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 427-443
Author(s):  
Gildas Tagny-Ngompé ◽  
Stéphane Mussard ◽  
Guillaume Zambrano ◽  
Sébastien Harispe ◽  
Jacky Montmain
Keyword(s):  
Law Enforcement ◽  
Success Rate ◽  
Text Classification ◽  
Binary Classification ◽  
Classification Algorithms ◽  
Classification Models ◽  
Target Variable ◽  
Technical Aspects ◽  
Legal Documents ◽  
Judicial Outcomes

This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions.

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