Bilingual Document Alignment with Latent Semantic Indexing
Table 1: Recall on the training and test data with known in-domain document pairs included in / excluded from the initial term-document matrix.
['[BOLD] performance on the training data', '[BOLD] performance on the training data', '[BOLD] performance on the training data', '[BOLD] performance on the training data  [BOLD] included', '[BOLD] performance on the training data  [BOLD] included', '[BOLD] performance on the training data  [BOLD] included', '[BOLD] performance on the training data  [BOLD] included', '[BOLD] performance on the training data  [BOLD] included', '[BOLD] performance on the training data  [BOLD] excluded', '[BOLD] performance on the training data  [BOLD] excluded', '[BOLD] performance on the training data  [BOLD] excluded', '[BOLD] performance on the training data  [BOLD] excluded', '[BOLD] performance on the training data  [BOLD] excluded']
To rank alignment hypotheses, we investigated all uniform linear combinations of the three individual scoring functions. In the first set of experiments on the training set, whose results are shown in the left half of the table, we used the list of known matches in the training data both for seeding cross-lingual LSI and evaluation. These numbers give us a sense how well monolingual documents are mapped into the joint semantic space by LSI and document fold-in. The first column of the recall numbers (“strict”) follows the official evaluation procedure, counting only exact URL matches as correct. The following columns show the performance if a more lenient notion of “matching documents” is applied. This more lenient measures computes the similarity between the expected and a proposed target document for a given source document (and vice versa) as follows: score(text1,text2)=2⋅lcss(text1,text2)|text1|+|text2| (4)