Challenge: a computational tool for Mapudüngun language is developed using finite state technology . it is the first of its kind for the language and is available as a web service for free .
Approach: They propose to develop a morphological and phonological machine for Mapudüngun using finite state technology.
Outcome: The proposed system is the first of its kind for the Mapuche language and is available for public use through a web interface.

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A Free/Open-Source Morphological Analyser and Generator for Sakha (2022.lrec-1)

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Challenge: a morphological transducer for Sakha is being developed for use in downstream tasks . the marginalised language is subject to increasing economic and cultural peril due to climate change .
Approach: They describe the development of a morphological analyser and generator for Sakha . the transducer has coverage of solidly above 90%, and high precision . it is already being used in downstream tasks such as linguistic maintenance .
Outcome: The proposed morphological analyser has coverage of 90% and high precision . it is already being used in computer assisted language learning applications .
A Finite-State Morphological Analyser for Evenki (2020.lrec-1)

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Challenge: Evenki is a language with rich morphology, therefore a morphological analyser is highly desirable for processing Evenki texts.
Approach: They propose to use a morphological analyser for Evenki to analyze half of the corpus . they evaluate the morphology of available corpora and estimate accuracy, recall and F-score .
Outcome: The proposed morphological analyser can analyse less than a half of the available corpora on Evenki . it is based on the Helsinki Finite-State Transducer toolkit (HFST).
An Unsupervised Method for Weighting Finite-state Morphological Analyzers (2020.lrec-1)

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Challenge: Morphological analysis is one of the tasks that have been studied for years.
Approach: They propose a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results.
Outcome: The proposed model weights a word2vec model using untagged corpora and captures the semantic meaning of the words.
Bootstrapping Techniques for Polysynthetic Morphological Analysis (2020.acl-main)

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Challenge: Polysynthetic languages have exceptionally large and sparse vocabularies due to the number of morpheme slots and combinations in a word.
Approach: They propose linguistically-informed approaches for bootstrapping a neural morphological analyzer . they use a finite state transducer to train an encoder-decoder model .
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Juman++: A Morphological Analysis Toolkit for Scriptio Continua (D18-2)

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Challenge: a morphological analyzer is useful for languages without natural word boundaries, but it is difficult to improve it without creating costly annotations.
Approach: They propose a toolkit for developing morphological analyzers for languages without natural word boundaries using lattices and neural nets.
Outcome: The proposed morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one.
Visualizing Inferred Morphotactic Systems (N19-4)

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Challenge: a web-based system facilitates the exploration of complex morphological patterns found in morphology rich languages.
Approach: They propose a web-based system that facilitates the exploration of complex morphological patterns found in morphology rich languages.
Outcome: The proposed system can be used to explore morphological patterns in morphology rich languages.
Web-based Annotation Interface for Derivational Morphology (2022.naacl-demo)

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Challenge: a visual interface for manual annotation of language resources for derivational morphology is created using relatively simple programming techniques.
Approach: They propose a web-based visual interface for manual annotation of language resources for derivational morphology.
Outcome: The proposed interface can be used for manual annotation of derivational morphology resources.
To compress or not to compress? A Finite-State approach to Nen verbal morphology (2020.acl-srw)

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Challenge: a transitive verb takes up to 1,740 unique features and is highly complex, with a morphological complexity of 80.3% . a finite-state approach has been used to build morphology and phonology resources for Nen, an underresourced language in Papua New Guinea.
Approach: They propose to use Finite-State methods to build a verbal morphological parser for an under-resourced Papuan language, Nen.
Outcome: The proposed model is half the size of the full decomposed model, while the 'Chunking' model is under half the scale of the decomposer, with an overall accuracy of 80.3%.
Finite-state morphological analysis for Gagauz (L18-1)

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Challenge: a finite-state approach to morphological analysis and generation of Gagauz is used . the model has a reasonable coverage over a range of freely-available corpora .
Approach: They propose a finite-state approach to morphological analysis and generation of Gagauz . they explicitly handle orthographic errors and variance, in addition to loan words .
Outcome: The proposed approach has a reasonable coverage over a range of freely-available corpora.
Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging (C18-1)

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Challenge: Korean is a highly agglutinative, character-rich language, requiring dictionary-less morphological analysis . a novel model can perform morphology and part-of-speech tagging without prior knowledge .
Approach: They propose a multi-stage action-based model that performs morphological transformation and part-of-speech tagging using arbitrary units of input.
Outcome: The proposed model achieves state-of-the-art word and sentence-level tagging accuracy with Korean corpus.

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