Document Type

Conference Paper

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

Computer Sciences

Publication Details

MultiLingMine 2016:Modeling, Learning and Mining for Cross/Multilinguality."Proceedings of the First Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine 2016) co-located with the 38th European Conference on Information Retrieval (ECIR 2016), Padova, Italy, 20 March

Abstract

Crosslingual document classification aims to classify documents written in different languages that share a common genre, topic or author. Knowledge-based methods and others based on machine translation deliver state-of-the-art classification accuracy, however because of their reliance on external resources, poorly resourced languages present a challenge for these type of methods. In this paper, we propose a novel set of language independent features that capture language use from a document at a deep level, using features that are intrinsic to the document. These features are based on vocabulary richness measurements and are text length independent and self-contained, meaning that no external resources such as lexicons or machine translation software are needed. Preliminary evaluation results show promising results for the task of crosslingual authorship attribution, outperforming similar methods.

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