Open Information Extraction for Mongolian Language

Lkhagvasuren, Ganchimeg; Rentsendorj, Javkhlan

doi:10.1007/978-981-13-9710-3_31

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Khaltar et al [42,43] created a method for extracting loan words from the Mongolian corpora. Lkhagvasuren and Rentsendorj built an open information extraction system that extracts relation tuples from Mongolian text [44]. In their research, Damiran and Altangerel tested (1) a decision tree algorithm [45] to classify Mongolian novels according to their authors and (2) a naive Bayesian classifier to classify Mongolian news articles [46].…”

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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“…1 The root of Mongolian is Altaic language family. Also, many researchers agree that Mongolian is related to Turkish and Korean [7].…”

Section: Introductionmentioning

confidence: 99%

Mongolian Part-of-Speech Tagging with Neural Networks

Lkhagvasuren

Rentsendorj

Namsrai

2021

Advances in Intelligent Information Hiding and Multimedia Signal Processing

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The purpose of this paper is to present a simple neural networks model-multilayer perceptron for Mongolian part-of speech tagging. We used about 1400 manually tagged sentences for training and testing from Mongolian Penn Treebank. The performance of the model is 80.78% which we consider a promising result. Also, another contribution of this work is that we make our testing data online for the sake of the development of Mongolian part-of-speech tagging research.

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