A corpus-based statistical Generalization Tree model is described to achieve rule opthnization for the information extraction task. First, the user creates specific rules for the target information from the sample articles through a training interface. Second, WordNet is applied to generalize noun entities in the specific rules. The degree of generalization is adjusted to fit the user's needs b...

In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation. To minimize the necessity of labeled data for refining detectors, previous work successfully made use of BabelNet, a semantic graph structure expressing relationships between synsets, as side information or prior knowledge. The goal of this paper is to enhance the use o...

An approach to feature extraction for texture images using optimal linear (autoregressive) predictors is presented. The features used for classiication are calculated from the prediction error using a local energy function. Experimental results are given to show the applicability of the method.

In this paper, we propose a new approach to estimate curvature information of point-sampled surfaces. We estimate curvatures in terms of the extremal points of a one-dimensional energy function for discrete surfels (points equipped with normals) and a multi-dimensional energy function for discrete unstructured point clouds. Experimental results indicate that our approaches can estimate curvatur...

Brain Computer Interface (BCI) systems still suffer from lack of accuracy in real-time applications. This problem emerges from isolated optimization, and in some occasions from mismatching of feature extraction and classification stages. To unify optimization of both stages, this paper presents a novel scheme to integrate them and simultaneously optimize under a unit criterion. The proposed met...

The extraction of statistically independent components from high-dimensional multi-sensory input streams is assumed to be an essential component of sensory processing in the brain. Such independent component analysis (or blind source separation) could provide a less redundant representation of information about the external world. Another powerful processing strategy is to extract preferentiall...

1. Abstract This paper presents a topology optimization method using the lattice Boltzmann method for the design of a flow channel considering two-phase fluid flows. This approach enables the design of fluidic devices such as two-phase microchannels that achieve a desired flow with maximal performances such as mixing and reaction, and extraction efficiencies. The optimization problems are formu...

In pulping and papermaking, dirt particles significantly affect the quality of paper. Knowledge of the dirt type helps to track the sources of the impurities which would considerably improve the paper making process. Dirt particle classification designed for this purpose should be adaptable because the dirt types are specific to the different processes of paper mills. This paper introduces a ge...

We propose a framework to improve the performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We further extend this framework to make a novel use of document structure: in some small, wellstructured corpora, sections can be identified that correspond to relation arguments, and distantly-labele...

In consideration of the previous workshop, we participate in TSC-3 to make improvements on important sentence extraction used in dry run of TSC-2. We formulate important sentence extraction as a combinational optimization problem that determines a set of sentences containing as many important information fragments as possible. In addition to the extraction method, we reinforce peripheral compon...