document: Cross-language information retrieval using PARAFAC2 A standard approach to cross-language information retrieval ( CLIR ) uses Latent Semantic Analysis ( LSA ) in conjunction with a multilingual parallel aligned corpus . This approach has been shown to be successful in identifying similar documents across languages - or more precisely , retrieving the most similar document in one language to a query in another language . However , the approach has severe drawbacks when applied to a related task , that of clustering documents `` language-independently '' , so that documents about similar topics end up closest to one another in the semantic space regardless of their language . The problem is that documents are generally more similar to other documents in the same language than they are to documents in a different language , but on the same topic . As a result , when using multilingual LSA , documents will in practice cluster by language , not by topic . We propose a novel application of PARAFAC2 ( which is a variant of PARAFAC , a multi-way generalization of the singular value decomposition ( SVD ) ) to overcome this problem . Instead of forming a single multilingual term-by-document matrix which , under LSA , is subjected to SVD , we form an irregular three-way array , each slice of which is a separate term-by-document matrix for a single language in the parallel corpus . The goal is to compute an SVD for each language such that V ( the matrix of right singular vectors ) is the same across all languages . Effectively , PARAFAC2 imposes the constraint , not present in standard LSA , that the `` concepts '' in all documents in the parallel corpus are the same regardless of language . Intuitively , this constraint makes sense , since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations . We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem . From our results , we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering , but also for solving other problems in cross-language information retrieval .
doc_bio_tags: O B I O B O O O O O B I O O O O B I I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O B I O