natural languages, so that access to Web information across language barriers can be improved. That requires developing techniques for discovering and representing cross-lingual links on the Web of Data. In this paper we explore the different dimensions of such a problem and reflect on possible avenues

. The mapping of documents written in different languages to the same multilingual thesaurus furthermore allows cross-language document comparison.

. The mapping of documents written in different languages to the same multilingual thesaurus furthermore allows cross-language document comparison.

While Web spam training data exists in English, we face an expensive human labeling procedure if we want to filter a Web domain in a different language. In this paper we overview how existing content and link based classification techniques work, how models can be “translated ” from English

, for each cluster, related groups of articles published over the previous seven days, using a cosine of weighted terms. The system furthermore tracks related news across languages, in all language pairs involved. The cross-lingual news cluster similarity is based on a linear combination of three types

We propose a formal model of Cross-Language Information Retrieval that does not rely on either query translation or document translation. Our approach leverages recent advances in language modeling to directly estimate an accurate topic model in the target language, starting with a query

We present the finalized version of our cross-lingual search engine Melange, and results obtained by running it on WebCLEF topics in an attempt to solve Mixed Monolingual and Multilingual tasks. We concentrate on certain features of the system which are relevant to the CLIR field and which can

The term Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions

Wikipedia articles in different languages are connected by interwiki links that are increasingly being recognized as a valuable source of cross-lingual information. Unfortunately, large numbers of links are imprecise or simply wrong. In this paper, techniques to detect such problems are identified

.g., Japanese) while performing multi-lingual searches on the WWW. The system relies on two AI-based modules: a language identifier using statistic and rule-based pattern matching and a quick MT module. This paper introduces these modules and describes these roles in cross-lingual search on the WWW. 1