Highly accurate error-driven method for noun phrase detection

We present a new model for detection of noun phrases in unrestricted text, whose most outstanding feature is its flexibility: the system is able to recognize noun phrases similar enough to the ones given by the inferred noun phrase grammar. The system provides a probabilistic finite-state automaton able to recognize the part-of-speech tag sequences which define a noun phrase. The recognition flexibility is possible by using a very accurate set of rankings for the FSA transitions. These accurate rankings are obtained by means of an evolutionary algorithm, which works with both, positive and negative examples of the language, thus improving the system coverage while maintaining its precision. We have tested the system on different corpora and evaluated different aspects of the system performance. We have also investigated other ways of improving the performance such as the application of certain filters in the training sets. The comparison of our results with other systems has revealed a considerable performance improvement.