Enriching the crosslingual link structure of Wikipedia - A classification-based approach

This paper presents a classification-based approach with the goal of inferring new cross-language links in Wikipedia and shows that this approach has a recall of 70% with a precision of 94% for the task of learning cross- language links on a test dataset.

Abstract

The crosslingual link structure of Wikipedia represents a valuable resource which can be exploited for crosslingual natural language processing applications. However, this requires that it has a reasonable coverage and is furthermore accurate. For the specific language pair German/English that we consider in our experiments, we show that roughly 50% of the articles are linked from German to English and only 14% from English to German. These figures clearly corroborate the need for an approach to automatically induce new cross-language links, especially in the light of such a dynamically growing resource such as Wikipedia. In this paper we present a classification-based approach with the goal of inferring new cross-language links. Our experiments show that this approach has a recall of 70% with a precision of 94% for the task of learning cross-language links on a test dataset.