Fu Lee Wang

Fractal summarization for mobile devices to access large documents on the web

Wireless access with mobile (or handheld) devices is a promising addition to the WWW and traditional electronic business. Mobile devices provide convenience and portable access to the huge information space on the Internet without requiring users to be stationary with network connection. However, the limited screen size, narrow network bandwidth, small memory capacity and low computing power are the shortcomings of handheld devices. Loading and visualizing large documents on handheld devices become impossible. The limited resolution restricts the amount of information to be displayed. The download time is intolerably long. In this paper, we introduce the fractal summarization model for document summarization on handheld devices. Fractal summarization is developed based on the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional automatic summarization, which is ideal for wireless access. Three-tier architecture with the middle-tier conducting the major computation is also discussed. Visualization of summary on handheld devices is also investigated.

Hybrid Learning and Education

This book constitutes the refereed proceedings of the First International Conference on Hybrid Learning, ICHL 2008, held in Hong Kong, China, in August 2008. The 38 revised full papers presented together with 3 keynote lectures were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on hybrid education, model and pedagogies for hybrid learning, trends, pervasive learning, mobile and ubiquitous learning, hybrid learning experiences, hybrid learning systems, technologies, as well as contextual attitude and cultural effects.

Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy

We present a framework SenticRank to incorporate sentiment for personalized search.Content-based and collaborative sentiment ranking methods are proposed.We compare the proposed sentiment-based search with baselines experimentally.We study the influence of sentiment corpora by using some sentiment dictionaries.Sentiment-based information can significantly improve performance in folksonomy. In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.

Personalized search for social media via dominating verbal context

With the rapid development of Web 2.0 communities, there has been a tremendous increase in user-generated content. Confronting such a vast volume of resources in collaborative tagging systems, users require a novel method for fast exploring and indexing so as to find their desired data. To this end, contextual information is indispensable and critical in understanding user preferences and intentions. In sociolinguistics, context can be classified as verbal context and social context. Compared with verbal context, social context requires not only domain knowledge to build pre-defined contextual attributes but also additional user data. However, to the best of our knowledge, no research has addressed the issue of irrelevant contextual factors for the verbal context model. To bridge this gap, the dominating set obtained from verbal context is proposed in this paper. We present (i) the verbal context graph to model contents and interrelationships of verbal context in folksonomy and thus capture the user intention; (ii) a method of discovering dominating set that provides a good balance of essentiality and integrality to de-emphasize irrelevant contextual factors and to keep the major characteristics of the verbal context graph; and (iii) a revised ranking method for measuring the relevance of a resource to an issued query, a discovered context and an extracted user profile. The experimental results obtained for a public dataset illustrate that the proposed method is more effective than existing baseline approaches.

Generating Incidental Word-Learning Tasks via Topic-Based and Load-Based Profiles

Compared to intentional word learning, incidental word learning better motivates learners, integrates development of more language skills, and provides richer contexts. The effectiveness of incidental word learning tasks can also be increased by employing materials that learners are more familiar with or interested in. Here, the authors present a framework to generate incidental word learning tasks via load-based profiles measured through the involvement load hypothesis, and topic-based profiles obtained from social media. They also conduct an experiment on real participants and find that the proposed framework promotes more effective and enjoyable word learning than intentional word learning. This article is part of a special issue on social media for learning.

Social emotion classification of short text via topic-level maximum entropy model

With the rapid proliferation of Web 2.0, the identification of emotions embedded in user-contributed comments at the social web is both valuable and essential. By exploiting large volumes of sentimental text, we can extract user preferences to enhance sales, develop marketing strategies, and optimize supply chain for electronic commerce. Pieces of information in the social web are usually short, such as tweets, questions, instant messages, messages, and news headlines. Short text differs from normal text because of its sparse word co-occurrence patterns, which hampers efforts to apply social emotion classification models. Most existing methods focus on either exploiting the social emotions of individual words or the association of social emotions with latent topics learned from normal documents. In this paper, we propose a topic-level maximum entropy (TME) model for social emotion classification over short text. TME generates topic-level features by modeling latent topics, multiple emotion labels, and valence scored by numerous readers jointly. The overfitting problem in the maximum entropy principle is also alleviated by mapping the features to the concept space. An experiment on real-world short documents validates the effectiveness of TME on social emotion classification over sparse words.

The Load-Based Learner Profile for Incidental Word Learning Task Generation

In recent years, the popularity and prosperity of mobile technologies and e-learning applications offer brand-new learning ways for people. English, as the most widely used language and the essential communication skill for people in the ‘earth village’ nowadays, has been widely learned by speakers of other languages. The importance of word knowledge in learning a second language is broadly acknowledged in the second language research literature. However, comparing with incidental word learning, the intentional learning method has the shortages of motivating reduction, simple acquisition and contextual deficiency. To address these problems, in this paper, we therefore proposed an incidental word learning model for e-learning. In particular, we measure the load of various incidental word learning tasks from the perspective of involvement load hypothesis so as to construct load-based learner profiles. To increase the effectiveness of various word learning activities and motivate learners better, a task generation method is developed based on the load-based learner profile. Moreover, we conduct experiments on real participants, and empirical results of which have further verified the effectiveness of the task generation method and the enjoyment of word learning.

Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies, and Applications

The Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies, and Applications collects emerging research and pedagogies related to the convergence of teaching and learning methods. This significant resource provides access to the latest knowledge related to hybrid learning, discovered and written by an international gathering of e-learning experts.

Fractal summarization: summarization based on fractal theory

In this paper, we introduce the fractal summarization model based on the fractal theory. In fractal summarization, the important information is captured from the source text by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the original is produced iteratively using the contractive transformation in the fractal theory. User evaluation has shown that fractal summarization outperforms traditional summarization.

An information delivery system with automatic summarization for mobile commerce

Wireless access with handheld devices is a promising addition to the WWW and traditional electronic business. Handheld devices provide convenience and portable access to the huge information space on the Internet without requiring users to be stationary with network connection. Many customer-centered m-services applications have been developed. The mobile computing, however, should be extended to decision support in an organization. There is a desire of accessing most update and accurate information on handheld devices for fast decision making in an organization. Unfortunately, loading and visualizing large documents on handheld devices are impossible due to their shortcomings. In this paper, we introduce the fractal summarization model for document summarization on handheld devices. Fractal summarization is developed based on the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional automatic summarization, which is ideal for wireless access. The three-tier architecture with the middle-tier conducting the major computation is also discussed. Visualization of summary on handheld devices is also investigated. The automatic summarization, the three-tier architecture, and the information visualization are potential solutions to the existing problems in information delivery to handheld devices for mobile commerce.