7. Conclusions

In this figure, the average pseudoprecision obtained with the k in Table 7 is shown for each of the 12 queries and for each database. The results of the LSI via SVD method are shown on the left and on the right are those corresponding to LSI via SDD. Methods for collections with 670, 690, and 758 documents have been labeled with a circle, an asterisk, and a triangle, respectively. It is observed that in the two methods, the average of pseudoprecision for Query 5 is 0 and for Queries 8, 9, and 10, it is 1. In Query 2, LSI via SVD also had a score of 1, while LSI via SDD did so only for 670 documents. In the rest of the queries, there are averages that go up or down as documents are added to the database. In Query 12, for example, it is noted that the highest score is for 690 documents, decreases when the collection is increased to 758 and decreases again when there are barely 670 documents. From this, it is concluded that there is no direct or inverse relationship between the average pseudoprecision and the number of

Documents MAP

Table 9. Percentage of relevant documents and MAP for the different databases.

Existing Additions % de Doc. relevant SVD SDD — 10.74 0.7776 0.7046 20 10.86 0.8059 0.7428 88 10.29 0.8022 0.6523

Figure 5. Comparison of the LSI methods with respect to the average pseudoprecision when adding documents to the

On the other hand, in order to evaluate the performance of the methods considering all the queries, in Table 9, the MAP obtained in each database is presented when again using the k of

documents in the database.

the Table 6.

database.

138 Multilingualism and Bilingualism

The LSI method originally used the singular values decomposition (SVD) for the benefits that it has in terms of data representation in spaces of reduced dimension and other properties with respect to data filtering. This makes the SVD a powerful tool in IR and in CLIR. The semidiscrete decomposition (SDD), of which few investigations have been developed, has been successfully used in IR, and this research has shown that it is also useful in CLIR and that it is also comparable with the standard approach used by the SVD. Evidence of this is that


Therefore, it is concluded that although the LSI via SVD method has been widely used and is a powerful tool in CLIR, the LSI via SDD method results in an important and innovative alternative in information recovery tasks, since, in addition to achieving results comparable to those of the other method in the task of retrieving relevant information in multiple languages after consulting only one, and also has the benefit of saving large amounts of space when huge databases are stored.
