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Found 9 papers, 1 papers with code
Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language
The precision of the rule-based model tested on a corpus with real errors (81. 0%) is slightly better than the neural model (79. 4%).
Reusing a Multi-lingual Setup to Bootstrap a Grammar Checker for a Very Low Resource Language without Data
Very low resource languages like Lule Sámi with less than 3, 000 speakers need to hurry to build these tools, but do not have the big corpus data that are required for the construction of machine learning tools.
Unmasking the Myth of Effortless Big Data - Making an Open Source Multi-lingual Infrastructure and Building Language Resources from Scratch
Machine learning (ML) approaches have dominated NLP during the last two decades.
Morphological Disambiguation of South S\'ami with FSTs and Neural Networks
We present a method for conducting morphological disambiguation for South S{\'a}mi, which is an endangered language.
Morphological Disambiguation of South Sámi with FSTs and Neural Networks
We present a method for conducting morphological disambiguation for South S\'ami, which is an endangered language.
