Automatic morphological analysis of L-verbs in Palula
Abstract: This study is exploring the possibilities of automatic morphological analysis of L-verbs in the Palula language by the help from Finite-state technology and two-level morphology along with supervised machine learning. The type of machine learning used are neural Sequence to Sequence models. A morphological transducer is made with the Helsinki Finite-State Transducer Technology, HFST, toolkit covering the L-verbs of the Palula Language. Several Sequence to Sequence models are trained on sets of L-verbs along with morphological tagging annotation. One model is trained with a small amount of manually annotated data and four models are trained with different amounts of training examples generated by the Finite-State Transducer. The efficiency and accuracy of these methods are investigated. The Sequence to Sequence model trained on solely manually annotated data did not perform as well as the other models. A Sequence to Sequence model trained with training examples generated by the transducer performed the best recall, accuracy and F1-score, while the Finite-State Transducer performed the best precision score.
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