This paper presents the results of the preliminary experiments in the automatic extraction of definitions (for semi-automatic glossary construction) from usually unstructured or only weakly structured e-learning texts in Bulgarian, Czech and Polish. The extraction is performed by regular grammars over XML-encoded morphosyntacticallyannotated documents. The results are less than satisfying and we claim that the reason for that is the intrinsic difficulty of the task, as measured by the low interannotator agreement, which calls for more sophisticated deeper linguistic processing, as well as for the use of machine learning classification techniques.
The article discusses methods of improving the ways of applying Balanced Random Forests (BRFs), a machine learning classification algorithm, used to extract definitions from written texts. These methods include different approaches to selecting attributes, optimising the classifier prediction threshold for the task of definition extraction and initial filtering by a very simple grammar.
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