After submitting five runs at the bilingual English to Spanish subtask of WebCLEF, we observed that it is possible to reduce terms in the documents that conform the corpus of a CLIRS, not only by reducing the time needed for indexing but also by improving the precision of the results obtained by CLIRS.
Our method is linear in computational time, and therefore it can be used in practical tasks. Until now, results obtained in terms of MRR are very low, but findings show that by applying better techniques of English to Spanish translation of queries, results can be dramatically improved [6].
We were concerned with the impact of indexing reduction on CLIRS, and in the future we hope to improve other components of our CLIRS, for instance, the use of vector space model, in order to improve the MRR.
The TP technique has shown an effective use on diverse areas of NLP, and its best features for NLP, are mainly two: a high content of semantic information and the sparseness that can be obtained on vectors for document representation on models based on the vector space model. On the other hand, its language independence allows to use this technique in CLIRS, that is the matter of WebCLEF.