Recognition of Polish Derivational Relations Based on Supervised Learning Scheme
Maciej Piasecki, Radosław Ramocki, Marek Maziarz · 2012
The paper presents construction of Derywator -a language tool for the recognition of Polish derivational relations.It was built on the basis of machine learning in a way following the bootstrapping approach: a limited set of derivational pairs described manually by linguists in plWordNet is used to train Derivator.The tool is intended to be applied in semi-automated expansion of plWordNet with new instances of derivational relations.The training process is based on the construction of two transducers working in the opposite directions: one for prefixes and one for suffixes.Internal stem alternations are recognised, recorded in a form of mapping sequences and stored together with transducers.Raw results produced by Derivator undergo next corpus-based and morphological filtering.A set of derivational relations defined in plWordNet is presented.Results of tests for different derivational relations are discussed.A problem of the necessary corpus-based semantic filtering is analysed.The presented tool depends to a very little extent on the hand-crafted knowledge for a particular language, namely only a table of possible alternations and morphological filtering rules must be exchanged and it should not take longer than a couple of working days.