2024-03-29T13:10:15Z
https://kitami-it.repo.nii.ac.jp/oai
oai:kitami-it.repo.nii.ac.jp:00008938
2022-12-13T02:19:40Z
1:87
MiNgMatch?A Fast N-gram Model for Word Segmentation of the Ainu Language
Nowakowski, Karol
Ptaszynski, Michal
Masui, Fumito
open access
word segmentation
tokenization
language modelling
n-gram models
Ainu language
endangered languages
under-resourced languages
Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segmenter—a fast word segmentation algorithm, which reduces the problem of identifying word boundaries to finding the shortest sequence of lexical n-grams matching the input text. In order to validate our method in a low-resource scenario involving extremely sparse data, we tested it with a small corpus of text in the critically endangered language of the Ainu people living in northern parts of Japan. Furthermore, we performed a series of experiments comparing our algorithm with systems utilizing state-of-the-art lexical n-gram-based language modelling techniques (namely, Stupid Backoff model and a model with modified Kneser-Ney smoothing), as well as a neural model performing word segmentation as character sequence labelling. The experimental results we obtained demonstrate the high performance of our algorithm, comparable with the other best-performing models. Given its low computational cost and competitive results, we believe that the proposed approach could be extended to other languages, and possibly also to other Natural Language Processing tasks, such as speech recognition.
MDPI
2019
eng
journal article
VoR
https://kitami-it.repo.nii.ac.jp/records/8938
https://doi.org/10.3390/info10100317
2078-2489
Information
10
10
317
https://kitami-it.repo.nii.ac.jp/record/8938/files/Information 2019, 10(10), 317.pdf
application/pdf
362.8 kB
2020-11-02