The aim of this paper is to study the effect that the use of Basic English versus common English has on information extraction from online resources. The amount of online information available to the public grows exponentially, and is potentially an excellent resource for information extraction. The problem is that this information often comes in an unstructured format, such as plain text. In order to retrieve knowledge from this type of text, it must first be analysed to find the relevant details, and the nature of the language used can greatly impact the quality of the extracted information. In this paper, we compare triplets that represent definitions or properties of concepts obtained from three online collaborative resources (English Wikipedia, Simple English Wikipedia and Simple English Wiktionary) and study the differences in the results when Basic English is used instead of common English. The results show that resources written in Basic English produce less quantity of triplets, but with higher quality.

This paper presents the process of development and the characteristics of an evaluation collection for a personalisation system for digital newspapers. This system selects, adapts and presents contents according to a user model that define information needs. The collection presented here contains data that are cross-related over four different axes: a set of news items from an electronic newspaper, collected into subsets corresponding to a particular sequence of days, packaged together and cross-indexed with a set of user profiles that represent the particular evolution of interests of a set of real users over the given days, expressed in each case according to four different representation frameworks: newspaper sections, Yahoo categories, keywords, and relevance feedback over the set of news items for the previous day. This information provides a minimum starting material over which one can evaluate for a given system how it addresses the first two observations - adapting to different users and adapting to particular users over time - providing that the particular system implements the representation of information needs according to the four frameworks employed in the collection. This collection has been successfully used to perform some different experiments to determine the effectiveness of the personalization system presented.