for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.

for machine learning. We present an algorithm, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER is a bottom-up learning algorithm that incorporates techniques from several inductive logic

of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give

collection: first, all the pages in the collection contain structured data conforming to a common schema; second, the pages are generated using a common template. Our goal is to automatically extract structured data from a collection of pages described above, without any human input like manually generated

, including, without limitation, a representation or warranty as to accuracy, completeness, or appropriateness for any particular purpose. The draft BRS shall be revised as the development of the policy, market rules, regulatory approvals, and review of the business requirements progresses. The ISO assumes

Multiple classifier fusion may generate more accurate classification than each of the constituent classifiers. Fusion is often based on fixed combination rules like the product and average. Only under strict probabilistic conditions can these rules be justified. We present here a simple rule

to automatically extract such rules and also to automatically detect violations. Previous work in this direction focuses on simple function-pair based programming rules and additionally requires programmers to provide rule templates. This paper proposes a general method called PR-Miner that uses a data mining

Most work on unsupervised entailment rule acquisition focused on rules between templates with two variables, ignoring unary rules- entailment rules between templates with a single variable. In this paper we investigate two approaches for unsupervised learning of such rules and compare the proposed

We consider a bottom-up query-evaluation scheme in which facts of relations are allowed to have nonground terms. The Magic Sets query-rewriting technique is generalized to allow arguments of predicates to be treated as bound even though the rules do not provide ground bindings for those arguments

Preparing business rules templates and object role modelling for transformations