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MOTIVATION Automatic knowledge discovery and efficient information access such as named entity recognition and relation extraction between entities have recently become critical issues in the biomedical literature. However, the inherent difficulty of the relation extraction task, mainly caused by the diversity of natural language, is further compounded in(More)
The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field. Here, we suggest two kinds of kernel methods for(More)
Named entity (NE) recognition has become one of the most fundamental tasks in biomedical knowledge acquisition. In this paper, we present a two-phase named entity recognizer based on SVMs, which consists of a boundary identification phase and a semantic classification phase of named entities. When adapting SVMs to named entity recognition, the multi-class(More)
Three-dimensional user interfaces (3D UIs) support user tasks in many non-traditional interactive systems such as virtual environments and augmented reality. Although 3D UI researchers have been successful in identifying basic user tasks and interaction metaphors, evaluating the usability of 3D interaction techniques, and improving the usability of many(More)
Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical variations. This paper presents a two-phase biomedical NER consisting of term boundary detection and semantic labeling. By dividing the problem, we can adopt an effective model(More)
Data Grid technology promises geographically distributed scientists to access and share physically distributed resources such as compute resource, networks, storage, and most importantly data collections for large-scale data intensive problems. The massive size and distributed nature of these datasets poses challenges to data intensive applications.(More)
Data grid technology promises geographically distributed scientists to access and share physically distributed resources such as compute resource, networks, storage, and most importantly data collections for large-scale data intensive problems. Because of the massive size and distributed nature of these datasets, scheduling data grid applications must(More)
This paper presents an architecture and implementation for a dynamic OGSA-based grid service architecture that extends GT3 to support dynamic service hosting - where to host and re-host a service within the grid in response to service demand and resource fluctuation. Our model goes beyond current OGSI implementations in which the service is presumed to be(More)