Sa-Kwang Song

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Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term's role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting(More)
Text mining is a popular methodology for building Technology Intelligence which helps companies or organizations to make better decisions by providing knowledge about the state-of-the-art technologies obtained from the Internet or inside companies. As a matter of fact, the objects or events (so-called declarative knowledge) are the target knowledge that(More)
In any search-based digital library (DL) systems dealing with a non-trivial number of documents, users are often required to go through a long list of short document descriptions in order to identify what they are looking for. To tackle the problem, a variety of document organization algorithms and/or visualization techniques have been used to guide users(More)
We propose a semantic tagger that provides high level concept information for phrases in clinical documents. It delineates such information from the statements written by doctors in patient records. The tagging, based on Hidden Markov Model (HMM), is performed on the documents that have been tagged with Unified Medical Language System (UMLS), Part-of-Speech(More)
Lots of valuable textual information is used to extract relations between named entities from literature. Composite kernel approach is proposed in this paper. The composite kernel approach calculates similarities based on the following information: (1) Phrase structure in convolution parse tree kernel that has shown encouraging results. (2)(More)