Mongolian Spelling Error Correction Using Word NGram Method

Ivanov, Byambasuren; Musa, Markhamet; Dulamragchaa, Uuganbaatar

doi:10.1007/978-981-33-6757-9_13

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Jaimai and Chimeddorj utilized a hidden Markov model with a bigram [30]. Ivanov et al utilized a minimum edit distance and a word n-gram with a back-off to identify Mongolian spelling errors and suggest alternative correct words [31]. Batsuren et al built a Mongolian WordNet [32].…”

Section: Mongolian Nlpmentioning

confidence: 99%

Recognizing Textual Inference in Mongolian Bar Exam Questions

Khaltarkhuu,

Batjargal,

Maeda

2024

Applied Sciences

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This paper examines how to apply deep learning techniques to Mongolian bar exam questions. Several approaches that utilize eight different fine-tuned transformer models were demonstrated for recognizing textual inference in Mongolian bar exam questions. Among eight different models, the fine-tuned bert-base-multilingual-cased obtained the best accuracy of 0.7619. The fine-tuned bert-base-multilingual-cased was capable of recognizing “contradiction”, with a recall of 0.7857 and an F1 score of 0.7674; it recognized “entailment” with a precision of 0.7750, a recall of 0.7381, and an F1 score of 0.7561. Moreover, the fine-tuned bert-large-mongolian-uncased showed balanced performance in recognizing textual inference in Mongolian bar exam questions, thus achieving a precision of 0.7561, a recall of 0.7381, and an F1 score of 0.7470 for recognizing “contradiction”.

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Section: Mongolian Nlpmentioning

confidence: 99%

Recognizing Textual Inference in Mongolian Bar Exam Questions

Khaltarkhuu,

Batjargal,

Maeda

2024

Applied Sciences

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Text Classification of Modern Mongolian Legal Documents Using BERT Models

Khaltarkhuu,

Batjargal,

Maeda

2023

Int. J. As. Lang. Proc.

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This paper investigates the application of deep learning-based natural language processing techniques in modern Mongolian legal documents. In particular, we explore several methods that apply Bidirectional Encoder Representations from Transformers (BERT) models for classifying modern Mongolian legal documents. Based on our findings, we propose BERT-based models called LEGAL-BERT-Mongolian. We demonstrated two variants of LEGAL-BERT-Mongolian, i.e., uncased-LEGAL-BERT-Mongolian and cased-LEGAL-BERT-Mongolian, for classifying modern Mongolian legal documents. The uncased-LEGAL-BERT-Mongolian model achieved the best results, with a precision of 0.91, recall of 0.87, and F1 score of 0.89, whereas the cased-LEGAL-BERT-Mongolian model achieved a precision of 0.87, recall of 0.83, and F1 score of 0.85.

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Mongolian Spelling Error Correction Using Word NGram Method

Cited by 2 publications

References 4 publications

Recognizing Textual Inference in Mongolian Bar Exam Questions

Recognizing Textual Inference in Mongolian Bar Exam Questions

Text Classification of Modern Mongolian Legal Documents Using BERT Models

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