Information Extraction is the automatic extraction of facts from text, which includes detection of named entities, entity relations and events. Conventional approaches to Information Extraction try to find syntactic patterns based on deep processing of text, such as partial or full parsing. The problem these solutions have to face is that as deeper analysis is used, the accuracy of the result d...

Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis....

The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification’s algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM) which are a group of supervised classification algorithms tha...

In this paper, we mainly explore the effectiveness of two kernelbased methods, the convolution tree kernel and the shortest path dependency kernel, for Chinese relation extraction based on ACE 2007 corpus. For the convolution kernel, the performances of different parse tree spans involved in it for relation extraction are studied. Then, experiments with composite kernels, which are a combinatio...

BACKGROUND Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a...

This study examined if pre-germination altered the water content and water activity, contents of phytate, total phenolic, (±)-catechin, quercetin and total antioxidant capacity of almond  (Prunus dulcis)kernel. Raw almond kernels were submerged for 15 hours in water, 0.02 mol dm-3 phosphate buffer solution (pH 5.0) and 0.02 mol dm-3 phosphate buffer solution (pH 7.0) at 25 and 40ºC, respectivel...

Locally preserving projection (LPP) does not take advantage of the spatial correlation of pixels in the image, and the pixels are considered as independent pieces of information. In this paper, a kernel based manifold learning feature extraction method which considers spatial relationship of neighboring pixels, called supervised composite kernel locality preserving projection (SCKLPP), is propo...

This paper presents a machine learning approach to question classification. We have defined a kernel function based on latent semantic information acquired from unlabeled data. This kernel allows including external semantic knowledge into the supervised learning process. We have combined this knowledge with a bag-of-words approach by means of composite kernels to obtain state-of-the-art results...

In this paper we propose an extension of sequence kernels to the case where the symbols that define the sequences have multiple representations. This configuration occurs in natural language processing for instance, where words can be characterized according to different linguistic dimensions. The core of our contribution is to integrate early the different representations in the kernel, in a w...