A neural fuzzy system learning with linguistic teaching signals is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. At first, we propose a fivelayered neural network for the connectionist realization of a fuzzy inference system. Th...

Common users have changed from mere consumers to active producers of multimedia data content. Video editing plays an important role in this scenario, calling for simple segmentation tools that can handle fast-moving and deformable video objects with possible occlusions, color similarities with the background, among other challenges. We present an interactive video segmentation method, named FOM...

A new more informative and effective fuzzy discriminant analysis method based on fuzzy regression with point prototypes has been developed and applied on two relevant data sets (the classical Fisher’s Iris data set and a clinical data set concerning different diseases). The proposed fuzzy method is consistent with the supervised character of the original discriminant analysis method. The classi...

Fuzzy ARTMAP is one of the families of the neural network architectures bused on ART(Adaptive Resonance Theory) in which supervised learning can be curried out. However, it usually tends to create more categories than are actually needed. This often causes the so culled overfitting problem, namely the performunce of the networks in test set is not monotonically increasing with the additional tr...

A method for a fuzzy hierarchical structure design is presented. The proposed method uses data to design a structure of the fuzzy subsystems. The fuzzy structure is designed level by level from data thus developing an initial fuzzy model is avoided. The methods is tested on one real-world application the daily gas consumption prediction. Copyright c ©2005 IFAC

Named entity recognition (NER) is the process of seeking to locate atomic elements in text into predefined categories such as the names of persons, organizations and locations.Most existingNERsystems are based on supervised learning. This method often requires a large amount of labelled training data, which is very time-consuming to build. To solve this problem, we introduce a semi-supervised l...

Named entity extraction is a fundamental task for many knowledge engineering applications. Existing studies rely on annotated training data, which is quite expensive when used to obtain large data sets, limiting the effectiveness of recognition. In this research, we propose an automatic labeling procedure to prepare training data from structured resources which contain known named entities. Whi...

The rapidly growing biomedical literature has been a challenging target for natural language processing algorithms. One of the tasks these algorithms focus on is called named entity recognition (NER), often employed to tag gene mentions. Here we describe a new approach for this task, an approach that uses graphbased semi-supervised learning to train a Conditional Random Field (CRF) model. Bench...

This paper addresses the challenging information extraction problem of linking named entities in text to entries in a knowledge base. Our approach uses supervised learning to (a) rank candidate knowledge base entries for each named entity, (b) classify the top-ranked entry as the correct disambiguation or not, and (c) group together the named entities without a corresponding entry in the knowle...