Comparison of various approaches to tagging for the inflectional Slovak language
Lubomír Benko, Dasa Munkova, Mária Pappová, Michal Munk
TL;DR
This paper compares six automatic taggers for the inflectional Slovak language, finding that different taggers perform best for literary and non-literary texts.
Contribution
The study identifies optimal taggers for literary and non-literary Slovak texts, highlighting UDPipe2 and RNNTagger as top performers.
Findings
UDPipe2 outperformed other taggers in seven out of nine tagset positions for literary texts.
RNNTagger showed the highest performance in eight out of nine tagset positions for non-literary texts.
RNNTagger is recommended for both text types due to its strong inflectional handling.
Abstract
Morphological tagging provides essential insights into grammar, structure, and the mutual relationships of words within the sentence. Tagging text in a highly inflectional language presents a challenging task due to word ambiguity. This research aims to compare six different automatic taggers for the inflectional Slovak language, seeking for the most accurate tagger for literary and non-literary texts. Our results indicate that it is useful to differentiate texts into literary and non-literary and subsequently, based on the text style to deploy a tagger. For literary texts, UDPipe2 outperformed others in seven out of nine examined tagset positions. Conversely, for non-literary texts, the RNNTagger exhibited the highest performance in eight out of nine examined tagset positions. The RNNTagger is recommended for both types of the text, the best captures the inflection of the Slovak…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Advanced Text Analysis Techniques