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Author: search.filters.author.Aung, ThuraSubject: search.filters.subject.Myanmar language

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    myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging
    (2025-01-01)
    Thant, Kaung Lwin
    ;
    Nongpong, Kwankamol
    ;
    Thu, Ye Kyaw
    ;
    Aung, Thura
    ;
    Wai, Khaing Hsu
    Named Entity Recognition (NER) involves identifying and categorizing named entities within textual data. Despite its significance, NER research has often overlooked low-resource languages like Myanmar (Burmese), primarily due to the lack of publicly available annotated datasets. To address this, we introduce myNER, a novel word-level NER corpus featuring a 7-tag annotation scheme, enriched with Part-of-Speech (POS) tagging to provide additional syntactic information. Alongside the corpus, we conduct a comprehensive evaluation of NER models, including Conditional Random Fields (CRF), Bidirectional LSTM (BiLSTM)-CRF, and their combinations with fastText embeddings in different settings. Our experiments reveal the effectiveness of contextualized word embeddings and the impact of joint training with POS tagging, demonstrating significant performance improvements across models. The traditional CRF joint-task model with fastText embeddings as a feature achieved the best result, with a 0.9818 accuracy and 0.9811 weighted F1 score with 0.7429 macro F1 score. BiLSTM-CRF with fine-tuned fastText embeddings gets the best result of 0.9791 accuracy and 0.9776 weighted F1 score with 0.7395 macro F1 score.
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    Myanmar Text Grade-Level Prediction Using Statistical and Linguistics Features
    (2025-01-01)
    Wai, Khaing Hsu
    ;
    Nwe, Hlaing Myat
    ;
    Aung, Thura
    ;
    Thu, Kaung Khant Si
    ;
    Mon, Hsu Yee
    Readability assessment supports curriculum design and adaptive learning by estimating the difficulty of a text for readers at different proficiency levels. However, research on text readability for the Myanmar language remains unexplored, mainly due to the absence of labeled resources and established computational approaches. This paper presents the first comprehensive study on Myanmar text readability classification. We construct a grade-annotated corpus from official Myanmar school textbooks (Grades 1-12) and extract linguistic, statistical, and Myanmar-specific indicators. We then evaluate regression and classification baselines, text-only embeddings, and ensembles. We also adapt three classic readability formulas (LIX, Dale-Chall, Flesch) to Myanmar and empirically show that their score distributions overlap heavily across educational levels. Experimental results show that ensemble-based models achieved the best performance in predicting grade levels, demonstrating the effectiveness of our feature design and modeling framework. This work introduces the first readability dataset, modeling approaches, and benchmark results for the Myanmar language, providing a strong foundation for future research in readability prediction, low-resource Natural language processing (NLP), and educational text analysis.
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    myContradict: Semi-supervised Contradictory Sentence Generation for Myanmar language
    (2024-01-01)
    Thu, Ye Kyaw
    ;
    Nwe, Ei Myat
    ;
    Aung, Thura
    In this paper, we proposed a semi-supervised low-resource approach for generating contradictory sentences in the Myanmar language using the self-training perspective. This semi-supervised learning method leverages Bidirectional Long Short-Term Memory neural network (BiLSTM) and Transformer encoder-decoder models. All of the experiments were done in both syllable- and word-level tokens. We also investigated the importance of linguistic features in the Myanmar language for generating contradictory sentences. Our approach found improvement in BiLSTM models with and without POS Tag features for both tokenization schemes. The best contradictory sentence generation model is the syllable-level Transformer model using the self-training approach with only one iteration, which gained the chrF<sup>++</sup> score of 0.75. It also gained 0.86 Precision, 0.85 Recall, and 0.85 F1-score of ROUGE-L scores and 0.94 Precision, 0.94 Recall, and 0.94 F1-score of BERTScore.