Juan Manuel Rodríguez

Performance Evaluation of Knowledge Extraction Methods

This paper shows the precision, the recall and the F-measure for the knowledge extraction methods (under Open Information Extraction paradigm): ReVerb, OLLIE and ClausIE. For obtaining these three measures a subset of 55 newswires corpus was used. This subset was taken from the Reuters-21578 text categorization and test collection database. A handmade relation extraction was applied for each one of these newswires.

Evaluation of open information extraction methods using Reuters-21578 database

The following article shows the precision, the recall and the F1-measure for three knowledge extraction methods under Open Information Extraction paradigm. These methods are: ReVerb, OLLIE and ClausIE. For the calculation of these three measures, a representative sample of Reuters-21578 was used; 103 newswire texts were taken randomly from that database. A big discrepancy was observed, after analyzing the obtained results, between the expected and the observed precision for ClausIE. In order to save the observed gap in ClausIE precision, a simple improvement is proposed for the method. Although the correction improved the precision of Clausie, ReVerb turned out to be the most precise method; however ClausIE is the one with the better F1-measure.

Dynamic Textures Segmentation with GPU

This work addresses the problem of motion segmentation in video sequences using dynamic textures. Motion can be globally modeled as a statistical visual process know as dynamic texture. Specifically, we use the mixtures of dynamic textures model which can simultaneously handle different visual processes. Nowadays, GPU are becoming increasingly popular in computer vision applications because of their cost-benefit ratio. However, GPU programming is not a trivial task and not all algorithms can be easily switched to GPU. In this paper, we made two implementations of a known motion segmentation algorithm based on mixtures of dynamic textures. One using CPU and the other ported to GPU. The performance analyses show the scenarios for which it is worthwhile to do the full GPU implementation of the motion segmentation process.