E-Participation towards Legislation: The Case of the Philippines

This paper discusses the issues toward the development of an eParticipation framework contextualized to the Philippine setting for legislation and the development of an ICT system. The project aims to enhance citizen participation and community empowerment in two key roles of the legislature – law making and executive oversight. The project used the concepts of eTransformation and Rapid Application Development Approach (RAD) to identify issues that will affect the future deployment of eParticipation Systems

Artificial Intelligence and its Potential Adverse Impacts on the Philippine Economy

Recent developments in artificial intelligence (AI) and deep learning techniques are expected to reshape the nature of the working environment in many economic sectors through the automation of many white collar jobs. This technological breakthrough poses threats of job obsolescence in several industries, particularly for a labor abundant country such as the Philippines. With human capital as one of its largest resources, the services sector is a major contributor to the country’s economy, contributing around 60% of the total gross domestic product and employing about 22.8 million workers (Philippine Statistics Authority, 2017)

Feature-based subjectivity classification of Filipino text

Subjectivity classification classifies whether a text expresses an opinion or not. Though there are already existing works in this field especially for the English Language, no reports have been made if these approaches are indeed effective when adapted to the Filipino language. This research reports a feature-based approach for subjectivity classification using existing classifiers such as Naïve Bayes, Bagging, Multilayer perceptron and Random Forest Tree. Result shows that the Bagging classifier gave the best results with 64.7% accuracy. © 2012 IEEE

e-Participation towards legislation: The case of the Philippines

This paper discusses the issues toward the development of an eParticipation framework contextualized to the Philippine setting for legislation and the development of an ICT system. The project aims to enhance citizen participation and community empowerment in two key roles of the legislature – law making and executive oversight. The project used the concepts of eTransformation and Rapid Application Development Approach (RAD) to identify issues that will affect the future deployment of eParticipation Systems

Building web-based Filipino language learing tool for heritage learners

Due to the emigration of millions of Filipinos, the Filipino language is now recognized as one of the major heritage languages. Children of Filipino migrants are exposed to the Filipino language but they did not learn it completely due to emigration and/or formal education in language of country they now live in. Filipino is currently being taught in several States in the US, Guam, Japan, and some of Europe, with enrollees mostly of Philippine descent, or heritage learners. Though there are some teaching resources for Filipino language, these resources are not easily accessible and it is not intended for heritage learners. This paper will discuss salinlahi, a system that was built to cater the needs of the Filipino heritage learners

A hybrid agent for automatically determining and extracting the 5Ws of Filipino news articles

Copying permitted for private and academic purposes. As the number of sources of unstructured data continues to grow exponentially, manually reading through all this data becomes notoriously time consuming. Thus, there is a need for faster understanding and processing of this data. This can be achieved by automating the task through the use of information extraction. In this paper, we present an agent that automatically detects and extracts the 5Ws, namely the who, when, where, what, and why from Filipino news articles using a hybrid of machine learning and linguistic rules. The agent caters specifically to the Filipino language by working with its unique features such as ambiguous prepositions and markers, focus instead of subject and predicate, dialect influences, and others. In order to be able to maximize machine learning algorithms, techniques such as linguistic tagging and weighted decision trees are used to preprocess and filter the data as well as refine the final results. The results show that the agent achieved an accuracy of 63.33% for who, 71.38% for when, 58.25% for where, 89.20% for what, and 50.00% for why. Copyright © by the paper\u27s authors