Information Retrieval and Large Text Structured Corpora

Conventional Information Retrieval Systems (IRSs), also called text indexers, deal with plain text documents or ones with a very elementary structure. These kinds of system are able to solve queries in a very efficient way, but they cannot take into account tags which mark different sections, or at best this capability is very limited. In contrast with this, nowadays, documents which are part of a corpus often have a rich structure. They are structured using XML (Extensible Markup Language) [1] or in some other format which can be converted to XML in a more or less simple way. So, building classical IRSs to work with these kinds of corpus will not benefit from this structure and results will not be improved. In addition, several of these corpora are very large and include hundreds or thousands of documents which in turn include millions or hundreds of millions of words. Therefore, there is the need to build efficient and flexible IRSs which work with large structured corpora. There are several examples of IRSs based on corpora [2] [3], of search methods over large corpora [4], and Chaudhri et al. [5] even introduce a review of different technologies that can be used to build generic IRSs based on XML. However, there are no comparative analyses or studies about technologies that can be used to build IRSs based on large structured corpora. Since these IRSs can be wide ranging, in this work we will focus on those which work with corpora that do not include any morphosyntactic annotation and are structured in XML format. All topics studied in this paper will also be useful for annotated corpora (although the study need to be completed for the latter) or for corpora without XML format (because if corpora are correctly structured, they can be easily converted to XML format).