Cheng-Yu Lu
Academia Sinica
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Publication
Featured researches published by Cheng-Yu Lu.
computational science and engineering | 2010
Cheng-Yu Lu; William W. Y. Hsu; Hsing-Tsung Peng; Jen-Ming Chung; Jan-Ming Ho
This study proposes an emotion detection engine for real time Internet chatting applications. We adopt a Web scale text mining approach that automates the categorization of affection state of daily events. We first accumulated a huge collection of real-life entities from Web that would participate in events with a user in the chatting room. Based on the common actions between each entity and the type of the user in a chatting room session, such as boy, girl, old man and so on, each collected entity was automatically classified into different affective categories such as pleasant, provoking, grievous, and scary. During a chatting session, each sentence is first parsed using semantic roles labeling techniques to retrieve the verb and object of the event embedded in the sentence. Based on a set of manually authored emotion generation rule, the system then assigns the emotion based on the verb and the affective categories of the object. Primitive evaluations show that the precision rate of the emotion detection engine is rather satisfactory for applications that distinguish emotions of Happiness, Sadness, Anger, and Fear.
web intelligence | 2011
Chi-Jen Wu; Jen-Ming Chung; Cheng-Yu Lu; Hahn-Ming Lee; Jan-Ming Ho
Scholars usually spend great deal of time on searching and reading papers of key researchers. However, to objectively determine key researcher of a topic relies on several measurements, such as publication, citation, recent academic activities. In this paper, a prototype of scholars searching and recommendation system based on a web mining approach in expert finding system is proposed. The system gives and recommends the ranking of scholars and turns out top-k scholars. A new ranking measure is designed, namely p-index, to reveal the scholar ranking of a certain field. We use a real-world dataset to test the robustness, the experiment results show our approach outperforms other existing approaches and users are highly interested in using the system again.
web intelligence | 2011
Cheng-Yu Lu; Shou-Wei Ho; Jen-Ming Chung; Fu-Yuan Hsu; Hahn-Ming Lee; Jan-Ming Ho
Ontology is essential in the formalization of domain knowledge for effective human-computer interactions (i.e., expert-finding). Many researchers have proposed approaches to measure the similarity between concepts by accessing fuzzy domain ontology. However, engineering of the construction of domain ontologies turns out to be labor intensive and tedious. In this paper, we propose an approach to mine domain concepts from Wikipedia Category Network, and to generate the fuzzy relation based on a concept vector extraction method to measure the relatedness between a single term and a concept. Our methodology can conceptualize domain knowledge by mining Wikipedia Category Network. An empirical experiment is conducted to evaluate the robustness by using TREC dataset. Experiment results show the constructed fuzzy domain ontology derived by proposed approach can discover robust fuzzy domain ontology with satisfactory accuracy in information retrieval tasks.
international conference on technologies and applications of artificial intelligence | 2011
Chun-Han Chen; Sushilata Devi Mayanglambam; Fu-Yuan Hsu; Cheng-Yu Lu; Hahn-Ming Lee; Jan-Ming Ho
Survey of academic literature or papers should be considered with both relevance and importance of references. Authors cite related references by considering integrity and novelty. However, the state-of-art publicly academic search engines and services can only recommend related papers of a certain topic. It shows to manually evaluate the novelty of the recommended papers is necessary. In this paper, we propose a citation-network-based methodology, namely Citation Authority Diffusion (CAD), to rapidly mine the limited key papers of a topic, and measure the novelty on literature survey. A defined Authority Matrix (
information reuse and integration | 2011
Jen-Ming Chung; Fu-Yuan Hsu; Cheng-Yu Lu; Hahn-Ming Lee; Jan-Ming Ho
AM
Expert Systems With Applications | 2012
Hsin-Tsung Peng; Cheng-Yu Lu; William W. Y. Hsu; Jan-Ming Ho
) is used to standardize duplication rate of authors and to describe the authority relation between the citing and the cited papers. Based on
ICSSE | 2015
William W. Y. Hsu; Yi-Wen Wu; Min-Ruey You; Cheng-Hsin Liao; Cheng-Yu Lu; Hao-Hsun Wang
AM
international conference on information science, electronics and electrical engineering | 2014
Yi-Wen Wu; William W. Y. Hsu; Mu-En Wu; Cheng-Yu Lu
, our
international conference on internet and web applications and services | 2012
Jen-Ming Chung; William W. Y. Hsu; Cheng-Yu Lu; Kuo-Ping Wu; Hahn-Ming Lee; Jan-Ming Ho
CAD
international conference industrial engineering other applications applied intelligent systems | 2012
Cheng-Yu Lu; William W. Y. Hsu; Jan-Ming Ho
methodology leverages the Belief Propagation to diffuse the authority among the citation network. Therefore, CAD transforms the converged citation network to a novelty paper list to researchers. The experimental results show CAD can mine more novelty papers by using real-world cases.