Papers by Yo Sato
Dialect Clustering with Character-Based Metrics: in Search of the Boundary of Language and Dialect (2020.lrec-1)
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| Challenge: | 'A language is a dialect with an army and navy' is attributed to sociologist Max Weinrich. |
| Approach: | They propose a universal character-based method for representing sentences so that one can calculate the distance between any two sentence pairs. |
| Outcome: | The proposed method can be used to calculate distance between two sentences by clustering a dialect/sub-language mixed corpus into sub-groups and to partially answer the question of what separates languages from dialects. |
Disambiguating Homographs and Homophones Simultaneously: A Regrouping Method for Japanese (2024.lrec-main)
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| Challenge: | Using a method that re-groups surface forms into clusters representing synonyms, we examine how accurate such disambiguation can be. |
| Approach: | They propose to regroup homographs and homophones into clusters and use them to disambiguate them. |
| Outcome: | The proposed method is applied post-hoc to trained word embeddings in Japanese. |
Homonym normalisation by word sense clustering: a case in Japanese (2020.coling-main)
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| Challenge: | homonyms and homophones are a problem in language processing because of their distinct meanings. |
| Approach: | They propose a method that uses contextualised embeddings to cluster tokens into distinct sense groups and use these groups to normalise synonymous instances to a single representative form. |
| Outcome: | The proposed method is able to normalise synonymous instances to a single representative form in Japanese and improves on normalisation and transliteration. |
Creating dialect sub-corpora by clustering: a case in Japanese for an adaptive method (L18-1)
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| Challenge: | a mixed corpus composed of different dialects is sufficiently resourced to cluster them into dialects. |
| Approach: | They propose a pipeline to derive clusters of dialects from a mixed corpus when their standard counterpart is sufficiently resourced. |
| Outcome: | The proposed pipeline can identify dialectal content when its standard counterpart is sufficiently resourced and can then cluster it into four dialects. |