Morphological parsing of low‑resource languages
It this paper we study morphological parsing and lemmatization on the material of Evenk and Selkup language. We compare basic neural models with their extensions that attempt to utilize additional linguistic information from the training data. We show that the augmented model does not improve over the baseline even decreasing performance for the task of lemmatization. We hypothesize that to be helpful additional information should be extracted from external resources, if available, not the corpus itself.
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