Textual Data Mining through the Synergistic Combination of Classifiers and Linguistic Processors

Numerical data mining tools are generally quite robust but only provide coarse-granularity results; such tools can handle very large inputs. Computational linguistic tools are able to provide fine-granularity results but are less robust; such tools, often semi-automatic, usually handle relatively short inputs. A synergistic combination of both types of tools is the basis of our hybrid approach. First, a connectionist classifier is used to locate potentially interesting documents, or segments thereof. Second, the user selects segments that will be forwarded to the linguistic processor in order to semi-automatically analyse their textual data and extract relevant information or knowledge elements. We present the main characteristics of our hybrid approach to textual data mining, plus a methodology by which it can be put to use. We also report on the results of a first evaluation involving a corpus made up of two texts pertaining to two