Klasifikátor morfů

John, Vojtěch

Morphological classification is the task of classifying morphs - the forms of morphemes - in laready segmented words. Since there are more and greateer resources for morpholog- ical segmentation than for morphological classification, methods presented in this thesis could be used for enriching already existing resources or creating new ones. We propose several methods of morph classification in increasing order of granularity. Firstly, we present several unsupervised, semi-supervised and supervised methods of root identifica- tion (on eight languages with manually annotated data in UniSegments), using simple statistical methods, derivational tree databases and CNN-LSTM-CRF sequence classifi- cation. We sample the learning curve of the neural taggers and train joint models for morphological segmentation and classificaton. Further, we present supervised feature- independent morph classification and joint classification and segmentation CNN-LSTM- CRF models, trained and tested on Czech, Slovak and Russian. Finally, we proposed two simple methods of aligning morphological features to morphs. We also used interlin- ear glossed text databases to assign morphological functions to inflectional morphs using CNN-LSTM-CRF sequence classifiers. 1

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