Modification(MOD) is a term generally applied to personal computer games, especially first-person shooters, role-playing games and real-time strategy games. Mods are made by the general public or a developer, and can be entirely new games in themselves, but mods are not standalone software and require the user to have the original release in order to run. We use Creativity Theory as the study framework, and divide it into four parts- Domain-Relevant Skills, Creativity-Relevant Processes, Task Motivation, and Social Environment to analyze. We collect the MOD creators¡¦ thoughts and reflections as text. From this thesis we can learn: 1. What computer games are popular that game players would like to create the modifications with it in Taiwan and China ? 2. What kinds of themes that MOD creators would like to use? 3. What are the MOD creators¡¦ internal and external motivations? 4. How the social environment influences the MOD creators? Finally, We will discuss the effect of social environment on domain-relevant skills, creativity-relevant processes and task motivation. Modifications can help original computer games to continue life and even improve the richness of the game content. It would become the trend that game company supports the game editor in the future. The cost of game development could be reduced by creating with MOD creators and test with MOD players.

With respect to art, we mainly discuss the issue of 'space', and we generally believe that there exists the so-called 'art-space'. However, Heidegger considers art in a very different way which is phenomenological. With phenomenological reduction, he reinterprets how the art-space exists, and he also endows art with a brand new meaning, that is, ¡§opening¡¨ , which is the way one expresses oneself. Nonetheless, Heidegger explains art in a dialectical way, that is to say, the negative way. However, that is the reason for Chinese landscape painting to be painted. In this essay, I try to yield further evidence for Heidegger 's theory, by investigating into Chinese landscape painting. The 'art-space' is hidden by the 'blank' spot of the landscape painting, so it is not emptiness, but gathering 'space', 'space itself', and 'light' into a whole; at the same time, it shows truth, which is lived truth. The 'art-space' does not come from imitating, but from a 'text-space' between likeness and unlikeness.

A typical online knowledge-sharing environment would generate vast amount of formal knowledge elements or interactions that generally available as textual documents. Thus, an effective management of the ever-increasing volume of online knowledge documents is essential to organizational knowledge sharing. Reply-semantic relationships between knowledge documents may exist either explicitly or implicitly. Such reply-semantic relationships between knowledge documents, once discovered or identified, would facilitate subsequent knowledge access by providing a novel and more semantic retrieval mechanism. In this study, we propose a preliminary taxonomy of reply-semantic relationships for documents organized in reply-replied structures and develop a SEmantic Enrichment between Knowledge documents (SEEK) technique for automatically annotating reply-semantic relationships between reply-pair documents. Based on the content-based text categorization techniques and genre classification techniques, we propose and evaluate different feature-set models, combinations of keyword features, POS statistics features, and/or given/new information (GI/NI) features. Our empirical evaluation results show that the proposed SEEK technique can achieve a satisfactory classification accuracy. Furthermore, use of keyword and GI/NI features by the proposed SEEK technique resulted in the best classification accuracy for the Answer/Comment classification task. On the other hand, the use of keyword features only can best differentiate Explanation and Instruction relationships.

Question answering from a corpus of question-answer (QA) pairs accepts a user question in a natural language, and retrieves relevant QA pairs in the corpus. Most of existing question answering techniques are monolingual in nature. That is, the language used for expressing a user question is identical to that for the QA pairs in the corpus. However, with the globalization of business environments and advances in Internet technology, more and more online information and knowledge are stored in the question-answer pair format on the Internet or intranet in different languages. To facilitate users¡¦ access to these QA-pair documents using natural language queries in such a multilingual environment, there is a pressing need for the support of cross-lingual question answering (CLQA). In response, this study designs a thesaurus based CLQA technique. We empirically evaluate our proposed CLQA technique, using a monolingual question answering technique and a machine translation based CLQA technique as performance benchmarks. Our empirical evaluation results show that our proposed CLQA technique achieves a satisfactory effectiveness when using that of the monolingual question answering technique as a performance reference. Moreover, our empirical evaluation results suggest our proposed thesaurus based CLQA technique significantly outperforms the benchmark machine translation based CLQA technique.

With the rapid expansion of e-commerce, the Web has become an excellent source for gathering customer opinions (or so-called customer reviews). Customer reviews are essential for merchants or product manufacturers to understand general responses of customers on their products for product or marketing campaign improvement. In addition, customer reviews can enable merchants better understand specific preferences of individual customers and facilitates making effective marketing decisions. Prior data mining research mainly concentrates on analyzing customer demographic, attitudinal, psychographic, transactional, and behavioral data for supporting customer relationship management and marketing decision making and did not pay attention to the use of customer reviews as additional source for marketing intelligence. Thus, the purpose of this research is to develop an efficient and effective opinion summarization technique. Specifically, we will propose a semantic-based product feature extraction technique (SPE) which aims at improving the existing product feature extraction technique and is desired to enhance the overall opinion summarization effectiveness.

The central government and most of local governments in Taiwan have adopted the e-mail services to provide citizens for requesting services or expressing their opinions through Internet. Traditionally, these requests/opinions need to be manually classified into appropriate departments for service rendering. However, due to the ever-increasing number of requests/opinions received, the manual classification approach is time consuming and becomes impractical. Therefore, in this study, we attempt to apply text categorization techniques for constructing automatically a classification mechanism in order to establish an efficient e-government service portal. The purpose of this thesis is to investigate effectiveness of different text categorization methods in supporting automatic classification of service requests/opinions emails sent to Mayor¡¦s mailbox. Specifically, in each phase of text categorization learning, we adopt and evaluate two methods commonly employed in prior research. In the feature selection phase, both the maximal x2¡@statistic method and the weighted average x2¡@statistic method of x2¡@statistic are evaluated. We consider the Binary and TFxIDF representation schemes in the document representation phase. Finally, we adopt the decision tree induction technique and the support vector machines (SVM) technique for inducing a text categorization model for our target e-government application. Our empirical evaluation results show that the text categorization method that employs the maximal x2 statistic method for feature selection, the Binary representation scheme, and the support vector machines as the underlying induction algorithm can reach an accuracy rate of 77.28% and an recall and precision rates of more than 77%. Such satisfactory classification effectiveness suggests that the text categorization approach can be employed to establish an effective and intelligent e-government service portal.

Abstract The government actively takes steps on the development of local language in our country after foreseeing that the local language may be lost. However, some problem has arisen such as insufficient teachers, complexity of textbook, spelling systems, etc. Both teachers and students can¡¦t accommodate themselves to the policies. The greatest difficulty lies in the Hakka and aboriginal languate particularly. As a hakka, I am engaged on the study of hakka textbook. The study focus on the hakka languate textbook of Miaoli County, Kaohsiung City, Kaohsiung County. It considers related materials to analyze the curricular connotation of hakka language ,the teaching materials of hakka language, and the properties of different edition to compare the differences among the three editions. The conclusion and suggestion can be provided as a reference for improvement of textbook of hakka language.

Environmental scanning is an important process, which acquires and uses the information about events, trends, and relationships in an organization's external environment. It permits an organization to adapt to its environment and to develop effective responses to secure or improve their position in the future. Event detection technique that identifies the onset of new events from streams of news stories would facilitate the process of organization's environmental scanning. However, traditional feature-based event detection techniques, which identify whether a news story contains an unseen event by comparing the similarity of words between the news story and past news stories, incur some limitations (e.g., the features shown in news document cannot actually represent the event described in it.). Thus, in this study, we developed an information extraction-based event detection (NEED) technique that combines information extraction and text categorization techniques to address the problems inherent to traditional feature-based event detection techniques. The empirical evaluation results showed that the NEED technique outperformed the traditional feature-based event detection techniques in miss rate and false alarm rate and achieved comparable event association accuracy rate to its counterpart.

This thesis proposes a knowledge management technique, called knowledge map management system, which consists of three components: knowledge navigation, knowledge seeking, and learning history advisory. The knowledge navigation function helps knowledge seekers to understand the knowledge structure. Similarly, the knowledge-seeking component helps knowledge seekers to retrieve knowledge with its structure. The learning adviser generalizes the knowledge access pattern in problem solving history and advises knowledge in the problem solving process. In this study, we prototype the knowledge map management system to facilitate knowledge sharing activities. In order to keep the knowledge map up to date, we also propose the incremental approach to maintaining knowledge structure with the addition of knowledge artifacts. The knowledge map creation and maintenance functions are evaluated using documents collected from SCTNet and master thesis repository at National Central Library, and result in encouraging results.

The objective of this thesis is to reduce the training size for the Mandarin address inputting system and the Mandarin phrase recognition system. A set of two-word Mandarin phrases is developed by the balanced sieving and mixture training techniques. This greatly reduces the training data size for the systems. Hidden Markov model using both MFCC and LPCC features is proposed in this thesis. Speech-text alignment, frame overlapping and tone recognition are incorporated to increase the correct recognition rates. For the speaker-dependent case, any phrase in these two speech systems can be recognized within one second.