In recent years, a variety of Machine Learning (ML) techniques has been used to improve the portability of IE systems to new domains, as in SRV (Freitag, 1998), RAPIER (Califf and Mooney, 1997), LIEP (Huffman, 1996), CRYSTAL (Soderland et al., 1995) and WHISK (Soderland, 1999) .
some drawbacks remain in the portability of these systems: a) existing systems generally depend on the supported text style and learn IE-rules either for structured texts, semi-structured texts or free text , b) IE systems are mostly single-concept learning systems, c) consequently, an extractor (e.g., a rule set) is learned for each concept within the scenario in an independent manner, d) the order of execution of the learners is set manually, and so are the scheduling and way of combination of the resulting extractors, and e) focusing on the training data, the size of available training corpora can be inadequate to accurately learn extractors for all the concepts within the scenario 2.