The Democracy Cube as a Framework for Guiding Participatory Planning for Community-based IT Initiatives

Literature suggests there is a need to build more theoretically-informed understandings of the social processes implicated in participatory IT planning and implementation (Jakku & Thorburn, 2010). In this study, we explore the value of Archon Fung’s (2006) “democracy cube” as a framework for qualitatively examining the process we undertook for planning a community-based IT strategy. Our planning process involved consultations with multiple stakeholder groups across five different communities, as well as from other entities involved in disaster management, with the aim of surfacing factors that shaped local communities’ abilities to participate in disaster management activities. These factors, drawn from qualitative interviews and categorized using a SWOT framework, were subsequently translated into an IT strategy. In this paper, we revisit this process and examine it using Fung’s (2006) three dimensions of democratic participation as a lens: participant selection (our use of multiple stakeholder groups); communication and decision (our consultation process); and authority and power (how participant input drove our strategy). We use the framework to identify the specific practices that made IT planning participative, as well as those that made it nonparticipative. We also use our empirical data to explore ways that the framework can be enhanced

Statistics-based rule generation for Filipino style and grammar checking

Current research works in the area of corpus and computational linguistics are now data-driven. When dealing with data, there is a need to check sentences for variations and inconsistencies. Style and grammar checkers can be used for this purpose. However, recent technologies rely on manually developing rules, which is a time-consuming process and a herculean task. In this paper, a statistics based rule generation framework that can be used to learn spelling variations, affix usage, and common mistakes made is presented. As domain, this research is focused on the Filipino language, characterized as a language with high degree of inflection. Monolingual corpora, annotated documents, as well as a tagged data were collected. The monolingual corpus was modeled and machine learning was used to aid in detecting spelling variations the tagged data was processed and data association was applied to determine affix usage and a subset of the annotated documents was digitized and used as training data for a statistical machine translation engine to determine common mistakes made. A total of 396 variant pairs, 16 affix usage, and 22 phrase pairs were generated and transformed into rules. A subset of these linguistic phenomena was reported in the literature, an indication that the framework can be used to automate linguistic tasks. The proposed variant scoring matches the style proposed by Sentro ng Wikang Filipino (SWF) with 30% recall and matches the style proposed by the Komisyon sa Wikang (KWF) Filipino with 60% recall, an indication that the style proposed by KWF is more inclined with the variant scoring. As future work, a policy paper could be drafted in coordination with experts in language planning

Pattern matching refinements to dictionary-based code-switching point detection

This study presents the development and evaluation of pattern matching refinements (PMRs) to automatic code switching point (CSP) detection. With all PMRs, evaluation showed an accuracy of 94.51%. This is an improvement to reported accuracy rates of dictionary-based approaches, which are in the range of 75.22%-76.26% (Yeong and Tan, 2010). In our experiments, a 100-sentence Tagalog-English corpus was used as test bed. Analyses showed that the dictionary-based approach using part-of-speech checking yielded an accuracy of 79.76% only, and two notable linguistic phenomena, (1) intra-word code-switching and (2) common words, were shown to have caused the low accuracy. The devised PMRs, namely: (1) common word exclusion, (2) common word identification, and (3) common n-gram pruning address this and showed improved accuracy. The work can be extended using audio files and machine learning with larger language resources. © 2012 The PACLIC

Klik-suri sa Online Community ng mga Lesbiyana sa Facebook Gamit ang Critical Discourse Analysis (CDA) at Natural Language Processing (NLP) (Click-analysis of a Lesbian Online Community in Facebook Using the CDA and NLP)

This study aims to describe the discourse on the online community of lesbians in the Philippines. The organization of Lesbian Community or LESCOM was chosen as subject of the study based on high number of ‘likes’ they received in Facebook. The texts were automatically gathered, filtered, shortlisted, and harvested from their Facebook page using the Natural Language Processing (NLP). The theoretical framework of Critical Discourse Analysis (CDA) was used in analysing the data in order to describe online discourses that reflect the identity and community of lesbians based in the Philippines. The result of analysis were the following: [1] the emotions projected by exclamatory mark displayed their identity; [2] some plural words served as their voice for being group-oriented; [3] frequent usage of personal names manifested their inclusive community; and [4] social consciousness was part of their organizational agenda

Click-analysis of a lesbian online community in Facebook using the critical discourse analysis and natural language processing

This study aims to describe the discourse on the online community of lesbians in the Philippines. The organization of Lesbian Community or LESCOM was chosen as subject of the study based on high number of \u27likes\u27 they received in Facebook. The texts were automatically gathered, f iltered, shortlisted, and harvested from their Facebook page using the Natural Language Processing (NLP). The theoretical framework of Critical Discourse Analysis (CDA) was used in analysing the data in order to describe online discourses that reflect the identity and community of lesbians based in the Philippines. The result of analysis were the following: [1] the emotions projected by exclamatory mark displayed their identity; [2] some plural words served as their voice for being group-oriented; [3] frequent usage of personal names manifested their inclusive community; and [4] social consciousness was part of their organizational agenda

A bilingual chatbot using support vector classifier on an automatic corpus Engine dataset

Brands are shifting to digital services to cater to their customers who have been spending more time online. The technology that exists today enhances customer experience and actualizes customer expectations through virtual service agents or \u27e-service agents\u27 during real-time interactions. Brands in most countries have to deal with bilingual customers as globalization occurs. Business process outsourcing is among the Philippines\u27 top foreign exchange earner aside from overseas workers\u27 remittances. These Philippine companies offer customer service but are mostly left manned-needing constant supervision. As a solution, the researchers present a bilingual retail chatbot that could handle the two official languages of the Philippines, Filipino-based on Tagalog-and English, and their code-switching variant Taglish. The proposed bilingual retail chatbot uses k-fold grid search cross-validation on a dataset constructed by a bilingual automatic corpus engine and a combination of both (1) support vector classifier-for intent identification, and (2) hash set containment-for attribute identification. © 2020 IEEE

Retrieval of semantically similar Philippine Supreme Court case decisions using Doc2Vec

Drafting a case decision is an intensive task for lower court judges which may be a factor in the case backlogs problem of the Philippine judiciary. The search for similar Philippine Supreme Court case decisions is a common task done manually in the trial setting in order to support the decision of the judge. Doc2Vec, a common document embedding technique in NLP, and cosine similarity are implemented in order to automatically retrieve semantically similar case decisions. The model shows to have an accuracy of 80% and exhibits a strong positive correlation with the similarity scores of a legal domain expert. © 2019 IEEE