Papers by Mohammad Mamun Or Rashid

4 papers
BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla Lemmatizer (2023.findings-emnlp)

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Challenge: Lemmatization holds significance in both natural language processing (NLP) and linguistics due to the highly inflected nature and morphological richness of Bangla text.
Approach: They propose linguistic rules for lemmatization and utilize a dictionary along with the rules to design a lemma specifically for Bangla.
Outcome: The proposed system achieves 96.36% accuracy when tested against a manually annotated test dataset.
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)

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Challenge: Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent.
Approach: They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse.
Outcome: The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage.
Unicode Normalization and Grapheme Parsing of Indic Languages (2024.lrec-main)

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Challenge: Indic writing systems encode words as linear sequences of Unicode characters . authors propose a grapheme parser for Abugida text to normalize inconsistencies .
Approach: They propose a normalizer for normalizing inconsistencies caused by Unicode encoding schemes . grapheme parser for Abugida deconstructs words into visually distinct orthographic syllables .
Outcome: The proposed library is more efficient than the previously used IndicNLP normalizer . it deconstructs words into visually distinct orthographic syllables or complex graphemes .
BanNERD: A Benchmark Dataset and Context-Driven Approach for Bangla Named Entity Recognition (2025.findings-naacl)

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Challenge: In a cross-dataset evaluation, models trained on BanNERD consistently outperformed those trained on four existing Bangla NER datasets.
Approach: They propose to use Bangla as a language to create the most extensive human-annotated and validated Bangla NLP dataset.
Outcome: The proposed method outperforms existing methods on Bangla NER datasets and performs competitively on English datasets.

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