CLGVSM: Adapting Generalized Vector Space Model to Cross-lingual Document Clustering

Cross-lingual document clustering (CLDC) is the task to automatically organize a large collection of cross-lingual documents into groups considering content or topic. Different from the traditional hard matching strategy, this paper extends traditional generalized vector space model (GVSM) to handle cross-lingual cases, referred to as CLGVSM, by incorporating cross-lingual word similarity measures. With this model, we further compare different word similarity measures in cross-lingual document clustering. To select cross-lingual features effectively, we also propose a softmatching based feature selection method in CLGVSM. Experimental results on benchmarking data set show that (1) the proposed CLGVSM is very effective for cross-document clustering, outperforming the two strong baselines vector space model (VSM) and latent semantic analysis (LSA) significantly; and (2) the new feature selection method can further improve CLGVSM.

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