Web information extraction is a fundamental issue for web information management and integrations. A common approach is to use wrappers to extract data from web pages or documents. However, a critical issue for wrapper development is how to generate extraction rules. In this paper, we propose a novel two-phase rule generation and optimization (2P-RULE) approach for wrapper generation. 2P-RULE c...

Information extraction from unstructured text data has been used essentially to provide new insights by collecting, storing, and analyzing text data in textual analysis. The research on event extraction has been recently getting more attention in information extraction area since lots of events happens and significantly affect our societies and countries. Related studies on event extraction use...

Rule-extraction from trained neural networks has previously been used to generate propositional rule-sets. The extraction of "generic" rules or objects from trained feedforward networks is clearly desirable and sufficient for many applications. We present several approaches to generate a knowledge base that includes rules, facts and a is-a hierarchy that enables the greater explanatory capabili...

Translation rule extraction is an important issue in syntax-based Statistical Machine Translation (SMT). Recent studies show that rule coverage is one of the key factors affecting the success of syntaxbased systems. In this paper, we first present a simple and effective method to improve rule coverage by using multiple parsers in translation rule extraction, and then empirically investigate the...

Global rule induction technique has been successfully used in information extraction (IE) from text documents. In this paper, we employ global rule induction technique to perform information extraction from news video documents. We divide our framework into two levels: shot; and story levels. We use a hybrid algorithm to classify each input video shot into one of the predefined genre types and ...

Rule extraction is an important task in knowledge discovery from imperfect training dataset in uncertain environments such as medical diagnosis. In a medical classification system for diagnosis, we cope with expensive or lack of expert knowledge in the design of the classifier. This paper presents an evolutionary fuzzy approach for tackling the problem of uncertainty in the process of rule extr...

Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA R...

Adaptivity to non-stationary contexts is a very important property for intelligent systems in general, as well as to a variety of applications of knowledge based systems in the area of Electric Power Systems. In this paper we present an innovative Neural-Fuzzy architecture that exhibits three important properties: online adaptation, knowledge (rule) modeling, and knowledge extraction from numer...