NerAxom-MGF: Leveraging Multi-Granularity Fusion for Assamese Named Entity Recognition
DOI:
https://doi.org/10.54195/irrj.26376Keywords:
Named Entity Recognition, Assamese , Low-Resource NLP, Multi-Granularity FusionAbstract
We introduce MGF-NER, a multi-granularity fusion architecture that integrates character-level, subword-level, and entity-span-level representations for the same task using NerAxom dataset. NerAxom comprises 102,857 tokens across 4,178 sentences annotated under the BIO scheme with seven entity categories: Person, Location, Organization, Date, Number, Occupation, and Work-of-Art. MGF-NER combines a Character-level Convolutional Neural Network that captures morpheme-level cues attenuated by subword fragmentation, a pre-trained MuRIL transformer for cross-lingually pretrained contextual semantics, an Entity Span Attention module for multi-word boundary detection, and a Conditional Random Field decoder that enforces valid BIO transitions. Under a seeded five-run evaluation protocol on the NerAxom test set, MGF-NER achieves a mean micro-averaged F1 of 85.45 ± 0.46, outperforming the strongest single-encoder baseline MuRIL (83.88±0.79) by 1.57 F1 points; the improvement is statistically significant under a paired sentence-level Wilcoxon signed-rank test (p = 0.005). We benchmark MGF-NER against nine baselines spanning traditional feature-based sequence labelling (CRF), a recurrent baseline (BiLSTM-CRF), language-agnostic multilingual transformers (mBERT, XLM-RoBERTa base and large, DistilBERT-multi), a script-similar Bangla-specialised transformer (Bangla-BERT), and Indic-specialised transformers (IndicBERTv2). Single-component and compound-component ablations, together with per-category ablation, decompose the gain across the four architectural components, and error analysis characterises the residual error profile.
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N Abinaya, Neethu John, Barathi HB Ganesh, Anand M Kumar, and KP Soman. AMRITA_CEN@ FIRE-2014: named entity recognition for Indian languages using rich features.In Proceedings of the 6th Annual Meeting of the Forum for Information Retrieval Evaluation,pages 103–111, 2014.
Gustavo Aguilar, Suraj Maharjan, Adrian Pastor López Monroy, and Thamar Solorio. A multitask approach for named entity recognition in social media data. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 148–153, 2017.
Nasser Alshammari and Saad Alanazi. The impact of using different annotation schemes on named entity recognition. Egyptian Informatics Journal, 22(3):295–302, 2021.
Xigang Bao, Mengyuan Tian, Zhiyuan Zha, and Biao Qin. MPMRC-MNER: A unified MRCframework for multimodal named entity recognition based multimodal prompt. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management,pages 47–56, 2023.
Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M Sohel Rahman, and Rifat Shahriyar. BanglaBERT: Language model pretraining and benchmarks for low-resource language understanding evaluation in Bangla. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1318–1327,2022.
Krishnanjan Bhattacharjee, Swati Mehta, Ajai Kumar, Ria Mehta, Dweep Pandya, Pratik Chaudhari, Devika Verma, et al. Named entity recognition: A survey for Indian languages. In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), volume 1, pages 217–220. IEEE, 2019.
Daniel M Bikel, Scott Miller, Richard Schwartz, and Ralph Weischedel. Nymble: a high-performance learning name-finder. In fifth conference on applied natural language processing, pages 194–201, 1997.
Johan Bos, Valerio Basile, Kilian Evang, Noortje J Venhuizen, and Johannes Bjerva. TheGroningen Meaning Bank. In Handbook of linguistic annotation, pages 463–496. Springer, 2017.
Jonathan H Clark, Dan Garrette, Iulia Turc, and John Wieting. Canine: Pre-training an efficienttokenization-free encoder for language representation. Transactions of the Association for Computational Linguistics, 10:73–91, 2022.
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 8440–8451, 2020.
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. Template-based named entity recognition using BART. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1835–1845, 2021.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186, 2019.
Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M Khapra, Anoop Kunchukuttan, and Pratyush Kumar. Towards leaving no Indic language behind: Building monolingual corpora, benchmark and models for Indic languages. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12402–12426, 2023.
Cicero Dos Santos and Victor Guimaraes. Boosting named entity recognition with neural character embeddings. In Proceedings of the Fifth Named Entity Workshop, pages 25–33, 2015.
Ralph Grishman and Beth M Sundheim. Message understanding conference-6: A brief history. In COLING 1996 volume 1: The 16th international conference on computational linguistics, 1996.
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith. Don’t stop pretraining: Adapt language models to domains and tasks. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 8342–8360, 2020.
Md Zahidul Haque, Sakib Zaman, Jillur Rahman Saurav, Summit Haque, Md Saiful Islam, and Mohammad Ruhul Amin. B-ner: A novel bangla named entity recognition dataset with largest entities and its baseline evaluation. IEEE Access, 11:45194–45205, 2023.
Michael A Hedderich, Lukas Lange, Heike Adel, Jannik Strötgen, and Dietrich Klakow. A survey on recent approaches for natural language processing in low-resource scenarios. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2545–2568, 2021.
Zhiheng Huang, Wei Xu, and Kai Yu. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991, 2015.
Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul NC, Avik Bhattacharyya, Mitesh M Khapra, and Pratyush Kumar. IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages. In Findings of the association for computational linguistics: EMNLP 2020, pages 4948–4961, 2020.
Simran Khanuja, Diksha Bansal, Sarvesh Mehtani, Savya Khosla, Atreyee Dey, Balaji Gopalan, Dilip Kumar Margam, Pooja Aggarwal, Rajiv Teja Nagipogu, Shachi Dave, et al. Muril: Multilingual representations for Indian languages. arXiv preprint arXiv:2103.10730, 2021.
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. Neural architectures for named entity recognition. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 260–270, 2016.
Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. A unified MRC framework for named entity recognition. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 5849–5859, 2020.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
Xuezhe Ma and Eduard Hovy. End-to-end sequence labeling via bi-directional lstm-cnns-crf. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1064–1074, 2016.
CS Malarkodi, Elisabeth Lex, and Sobha Lalitha Devi. Named Entity Recognition for the Agricultural Domain. Research in Computer Science, 117(1):121–132, 2016.
Muhammad Kamran Malik and Syed Mansoor Sarwar. Named entity recognition system for postpositional languages: Urdu as a case study. International Journal of Advanced Computer Science and Applications, 7(10), 2016.
Andrew McCallum and Wei Li. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003, pages 188–191, 2003.
Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M Khapra, Pratyush Kumar, Rudra Murthy, and Anoop Kunchukuttan. Naamapadam: A large-scale named entity annotated data for Indic languages. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10441–10456, 2023.
Benjamin Muller, Antonios Anastasopoulos, Benoît Sagot, and Djamé Seddah. When being unseen from mBERT is just the beginning: Handling new languages with multilingual language models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 448–462, 2021.
Rudra Murthy, Anoop Kunchukuttan, and Pushpak Bhattacharyya. Judicious selection of training data in assisting language for multilingual neural NER. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 401–406, 2018.
David Nadeau and Satoshi Sekine. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1):3–26, 2007.
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. Cross-lingual name tagging and linking for 282 languages. In Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1946–1958, 2017.
Dhrubajyoti Pathak, Sukumar Nandi, and Priyankoo Sarmah. AsNER-annotated dataset and baseline for Assamese named entity recognition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6571–6577, 2022.
Matthew E Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih. Dissecting contextual word embeddings: Architecture and representation. In Proceedings of the 2018 conference on empirical methods in natural language processing, pages 1499–1509, 2018.
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder. Mad-x: An adapter-based framework for multi-task cross-lingual transfer. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pages 7654–7673, 2020.
Telmo Pires, Eva Schlinger, and Dan Garrette. How multilingual is multilingual BERT? In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 4996–5001, 2019.
Lev Ratinov and Dan Roth. Design challenges and misconceptions in named entity recognition. In Proceedings of the thirteenth conference on computational natural language learning (CoNLL-2009), pages 147–155, 2009.
Erik Tjong Kim Sang and Fien De Meulder. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003, pages 142–147, 2003.
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.
Punam Sarmah, Manash P Lahkar, Shobhanjana Kalita, and Utpal Sharma. NerAxom: A BIO-Tagged NER Dataset and HybridNeural-Rule Framework for Assamese. Engineering Research Express, 2026.
Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1715–1725, 2016.
Padmaja Sharma, Utpal Sharma, and Jugal Kalita. The first steps towards Assamese named entity recognition. In Brisbane Convention Center, volume 1, pages 1–11, 2010.
Padmaja Sharma, Utpal Sharma, and Jugal Kalita. Named entity recognition in Assamese using CRFS and rules. In 2014 International Conference on Asian Language Processing (IALP), pages 15–18. IEEE, 2014.
Padmaja Sharma, Utpal Sharma, and Jugal Kalita. Named entity recognition in Assamese: a hybrid approach. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 2114–2120. IEEE, 2016.
Jana Straková, Milan Straka, and Jan Hajic. Neural architectures for nested NER through linearization. In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 5326–5331, 2019.
Gitimoni Talukdar, Pranjal Protim Borah, and Arup Baruah. Supervised named entity recognition in Assamese language. In 2014 International Conference on Contemporary Computing and Informatics (IC3I), pages 187–191. IEEE, 2014.
Yu Wang, Hanghang Tong, Ziye Zhu, and Yun Li. Nested named entity recognition: a survey. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(6):1–29, 2022.
Frank Wilcoxon. Individual comparisons by ranking methods. In Breakthroughs in statistics: Methodology and distribution, pages 196–202. Springer, 1992.
Shijie Wu and Mark Dredze. Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 833–844, 2019.
Zexuan Zhong and Danqi Chen. A frustratingly easy approach for entity and relation extraction. In Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 50–61, 2021.
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