As a Member of the Ainu People

Shigeru, Kayano; Hane, Mikiso; Selden, Kyoko; Selden, Lili Iriye

doi:10.4324/9780429498534-13

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last 285 entries (roughly 10% of the dictionary, character-wise) were excluded from the training data, in order to use them as test data in evaluation experiments (see the next section). An online lexicon consisting of digitized versions of three Ainu-Japanese dictionaries [2,57,58], comprising a total of 33,126 entries. We used only the headwords included in the dictionary.…”

Section: Training Datamentioning

confidence: 99%

MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language

2019

View full text Add to dashboard Cite

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.

show abstract

Section: Training Datamentioning

confidence: 99%