Learning from Parsed Sentences with INTHELEX

In: Proceedings of CoNLL-2000 and LLL-2000, pages 194-198, Lisbon, Portugal, 2000. Learning from Parsed Sentences with INTHELEX F. Esposito and S. Ferilli and N. Fanizzi and G. Semeraro Dipartimento di Informatica Universit£ di Bari via E. Orabona, 4 - 70126 Bari - Italia {esposito, ferilli, fanizzi, semeraro}~di.uniba.it Abstract In the context of language learning, we address a logical approach to information extraction. The system INTHELEX, used to carry out this task, requires a logic representation of sentences to run the learning algorithm. Hence, the need for parsers to produce structured representa- tions from raw text. This led us to develop a prototypical Italian language parser, as a pre- processor in order to obtain the structured rep- resentation of sentences required for the sym- bolic learner to work. A preliminary exper- imentation proved that the logic approach to learning from language is able to capture the semantics underlying the kind of sentences that were processed, even if a comparison with clas- sical methods as regards efficiency has still to be done. 1 Introduction Language learning has gained growing attention in the last years. Statistical approaches, so far extensively used -- see (Saitta and Neri, 1997) for an overview of the research in this area --, have severe limitations, whereas the flexibility and expressivity of logical representations make them highly suitable for natural language anal- ysis (Cussens, 1999). Indeed, logical approaches may have a relevant impact at the level of se- mantic interpretation, where a logical represen- tation of the meaning of a sentence is important and useful (Mooney, 1999). Logical approaches have been already em- ployed in Text Categorization and/or Informa- tion Extraction. Yet they try to use an expres- sive representation language such as first-order logic to define simple properties about tex- tual sources, regarded, for instance, as bags of words (Junker et al., 1999) or as semi-structured texts (Freitag, 2000). These properties are of- ten loosely related with the grammar of the underlying language, often relying on extra- grammatical features (Cohen, 1996). We intend to exploit a logic representation for exploiting the grammatical structure of texts, as it could be detected using a proper parser. Indeed, a more knowledge intensive technique is likely to perform better applied on the tasks mentioned above. When no background knowledge about the language structure is assumed to be available, one of the fundamental problems with the adop- tion of logic learning techniques is that a struc- tured representation of sentences is required on which the learning algorithm can be run. Thus, the need arises for parsers that are able to discover such a structure starting from raw, unstructured text. Research in this field has produced a variety of tools and techniques for English, that cannot be applied to other lan- guages, such as Italian, because of the differ- ent, and sometimes much more complex, gram- matical structure. Such considerations led us to develop a prototypical Italian language parser, that could serve as a pre-processor of texts in order to obtain the structured representation …

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