Rini Wongso

Bio: Rini Wongso is an academic researcher from Binus University. The author has contributed to research in topics: Support vector machine & Automatic summarization. The author has an hindex of 6, co-authored 17 publications receiving 166 citations.

Summarizing Text for Indonesian Language by Using Latent Dirichlet Allocation and Genetic Algorithm

Abstract: The number of documents progressively increases especially for the electronic one. This degrades effectivity and efficiency in managing them. Therefore, it is a must to manage the documents. Automatic text summarization is able to solve by producing text document summaries. The goal of the research is to produce a tool to summarize documents in Bahasa: Indonesian Language. It is aimed to satisfy the user’s need of relevant and consistent summaries. The algorithm is based on sentence features scoring by using Latent Dirichlet Allocation and Genetic Algorithm for determining sentence feature weights. It is evaluated by calculating summarization speed, precision, recall, F-measure, and some subjective evaluations. Extractive summaries from the original text documents can represent important information from a single document in Bahasa with faster summarization speed compared to manual process. Best F-measure value is 0,556926 (with precision of 0.53448 and recall of 0.58134) and summary ratio of 30%.

此处“personality”译法探析

Abstract: 在翟象俊主编的《大学英语》第2册第5单元中，有这样一个句子：“As a boy and then as an adult, I never lost my wonder at the personality that was Einstein.”教参中指出“the personality that was Einstein”应理解为“the personality which was the most striking characteristic of Einstein”，该句译为“作为一个孩子，到后来作为一个成人，我一直对爱因斯坦的个性惊叹不已”。很明显，在这里译者把“personality”理解为“个性，人格”，但本人认为应译为“人物，名人”更妥。“personality”可作“个性，人格”讲，但它还有另外一个重要意思。在陆谷孙主编的《英汉大词典》（1993年版）中，“personality”第3条释义为：“个人，人物，名人”：“appoint a personality to lead a campaign，派一个人去领导一场运动”。“a personality in the news，新闻人物”。在...

Personality Predictions Based on User Behavior on the Facebook Social Media Platform

Abstract: With the development of social networks, a large variety of approaches have been developed to define users’ personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%.

Rediscovering the use of chatbots in education: A systematic literature review

Abstract: Chatbots have been around for years and have been used in many areas such as medicine or commerce. Our focus is on the development and current uses of chatbots in the field of education, where they can function as service assistants or as educational agents. In this research paper, we attempt to make a systematic review of the literature on educational chatbots that address various issues. From 485 sources, 80 studies on chatbots and their application in education were selected through a step‐by‐step procedure based on the guidelines of the PRISMA framework, using a set of predefined criteria. The results obtained demonstrate the existence of different types of educational chatbots currently in use that affect student learning or improve services in various areas. This paper also examines the type of technology used to unravel the learning outcome that can be obtained from each type of chatbots. Finally, our results identify instances where a chatbot can assist in learning under conditions similar to those of a human tutor, while exploring other possibilities and techniques for assessing the quality of chatbots. Our analysis details these findings and can provide a solid framework for research and development of chatbots for the educational field.

Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network

Abstract: The rapid growth of electronic documents are causing problems like unstructured data that need more time and effort to search a relevant document. Text Document Classification (TDC) has a great significance in information processing and retrieval where unstructured documents are organized into pre-defined classes. Urdu is the most favorite research language in South Asian languages because of its complex morphology, unique features, and lack of linguistic resources like standard datasets. As compared to short text, like sentiment analysis, long text classification needs more time and effort because of large vocabulary, more noise, and redundant information. Machine Learning (ML) and Deep Learning (DL) models have been widely used in text processing. Despite the major limitations of ML models, like learn directed features, these are the favorite methods for Urdu TDC. To the best of our knowledge, it is the first study of Urdu TDC using DL model. In this paper, we design a large multi-purpose and multi-format dataset that contain more than ten thousand documents organize into six classes. We use Single-layer Multisize Filters Convolutional Neural Network (SMFCNN) for classification and compare its performance with sixteen ML baseline models on three imbalanced datasets of various sizes. Further, we analyze the effects of preprocessing methods on SMFCNN performance. SMFCNN outperformed the baseline classifiers and achieved 95.4%, 91.8%, and 93.3% scores of accuracy on medium, large and small size dataset respectively. The designed dataset would be publically and freely available in different formats for future research in Urdu text processing.