Towards Malay named entity recognition: an open-source dataset and a multi-task framework

References

• Abinaya, N., John, N., Ganesh, B. H. B., Kumar, A. M., & Soman, K. P. (2014). AMRITA_CENFIRE-2014: named entity recognition for indian languages using rich features. In Proceedings of the forum for information retrieval evaluation (pp. 103–111). Association for Computing Machinery. https://doi.org/10.1145/2824864.2824882.
   Google Scholar
• Akbik, A., Bergmann, T., & Vollgraf, R. (2019). Pooled contextualized embeddings for named entity recognition. In J. Burstein, C. Doran, and T. Solorio (Eds.), Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human language technologies, NAACL-HLT 2019, June 2–7 (Vol. 1, pp. 724–728). Association for Computational Linguistics. https://doi.org/10.18653/v1/n19-1078.
   Google Scholar
• Akbik, A., Blythe, D., & Vollgraf, R. (2018). Contextual string embeddings for sequence labelling. In E. M. Bender, L. Derczynski, and P. Isabelle (Eds.), Proceedings of the 27th international conference on computational linguistics, COLING 2018, August 20–26 (pp. 1638–1649). Association for Computational Linguistics. https://aclanthology.org/C18-1139/.
   Google Scholar
• Alfina, I., Manurung, R., & Fanany, M. I. (2016). DBpedia entities expansion in automatically building dataset for Indonesian NER. In 2016 international conference on advanced computer science and information systems (ICACSIS) (pp. 335–340). IEEE. https://doi.org/10.1109/ICACSIS.2016.7872784.
   Google Scholar
• Alfina, I., Savitri, S., & Fanany, M. I. (2017). Modified DBpedia entities expansion for tagging automatically NER dataset. In 2017 international conference on advanced computer science and information systems (ICACSIS) (pp. 216–221). IEEE. https://doi.org/10.1109/ICACSIS.2017.8355036.
   Google Scholar
• Alfred, R., Leong, L. C., On, C. K., & Anthony, P. (2014). Malay named entity recognition based on rule-based approach. International Journal of Machine Learning and Computing, 4(3). https://doi.org/10.7763/IJMLC.2014.V4.428
   Google Scholar
• Aliod, D. M., van Zaanen, M., & Smith, D. (2006). Named entity recognition for question answering. In L. Cavedon and I. Zukerman (Eds.), Proceedings of the Australasian language technology workshop, ALTA 2006, November 30–December 1 (pp. 51–58). Australasian Language Technology Association. https://aclanthology.org/U06-1009/.
   Google Scholar
• Anbukkarasi, S., Varadhaganapathy, S., Jeevapriya, S., Kaaviyaa, A., Lawvanyapriya, T., & Monisha, S. (2022). Named entity recognition for tamil text using deep learning. In 2022 international conference on computer communication and informatics (ICCCI) (pp. 1–5). https://doi.org/10.1109/ICCCI54379.2022.9740745.
   Google Scholar
• Asmai, S. A., Salleh, M. S., Basiron, H., & Ahmad, S. (2018). An enhanced Malay named entity recognition using combination approach for crime textual data analysis. International Journal of Advanced Computer Science and Applications, 9(9). https://doi.org/10.14569/issn.2156-5570
   Google Scholar
• Chiu, J. P. C., & Nichols, E. (2016). Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, 4, 357–370. https://doi.org/10.1162/tacl_a_00104
   Google Scholar
• Dai, X., & Adel, H. (2020). An analysis of simple data augmentation for named entity recognition. In D. Scott, N. Bel, and C. Zong (Eds.), Proceedings of the 28th international conference on computational linguistics, COLING 2020, December 8–13 (pp. 3861–3867). International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.343.
   Google Scholar
• Derczynski, L., Nichols, E., van Erp, M., & Limsopatham, N. (2017). Results of the WNUT2017 shared task on novel and emerging entity recognition. In L. Derczynski, W. Xu, A. Ritter, and T. Baldwin (Eds.), Proceedings of the 3rd workshop on noisy user-generated text, nut@emnlp 2017, September 7 (pp. 140–147). Association for Computational Linguistics. https://doi.org/10.18653/v1/w17-4418.
   Google Scholar
• Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, and T. Solorio (Eds.), Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human language technologies, NAACL-HLT 2019, June 2–7 (Vol. 1, pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/n19-1423.
   Google Scholar
• Etzioni, O., Cafarella, M. J., Downey, D., Popescu, A., Shaked, T., Soderland, S., Weld, D. S., & Yates, A. (2005). Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence, 165(1), 91–134. https://doi.org/10.1016/j.artint.2005.03.001
   Web of Science ®Google Scholar
• Fu, Y., Lin, N., Lin, X., & Jiang, S. (2021). Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition. Journal of Intelligent & Fuzzy Systems, 41(1), 563–574. https://doi.org/10.3233/JIFS-202286
   Web of Science ®Google Scholar
• Gunawan, W., Suhartono, D., Purnomo, F., & Ongko, A. (2018). Named-entity recognition for Indonesian language using bidirectional LSTM-CNNs. Procedia Computer Science, 135, 425–432. The 3rd International conference on computer science and computational intelligence (ICCSCI 2018), empowering smart technology in digital era for a better life. https://www.sciencedirect.com/science/article/pii/S1877050918314832.
   Google Scholar
• Guo, J., Xu, G., Cheng, X., & Li, H. (2009). Named entity recognition in query. In J. Allan, J.A. Aslam, M. Sanderson, C. Zhai, and J. Zobel (Eds.), Proceedings of the 32nd annual international ACM SIGIR conference on research and development in information retrieval, SIGIR 2009, July 19–23 (pp. 267–274). ACM. https://doi.org/10.1145/1571941.1571989.
   Google Scholar
• Hedderich, M. A., Lange, L., Adel, H., Strötgen, J., & Klakow, D. (2021). A survey on recent approaches for natural language processing in low-resource scenarios. In K. Toutanova (Eds.), Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: Human language technologies, NAACL-HLT 2021, June 6–11 (pp. 2545–2568). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.201.
   Google Scholar
• Hedderich, M. A., Lange, L., & Klakow, D. (2021). ANEA: Distant supervision for low-resource named entity recognition. CoRR abs/2102.13129. https://arxiv.org/abs/2102.13129.
   Google Scholar
• Hovy, E. H., Marcus, M. P., Palmer, M., Ramshaw, L. A., & Weischedel, R. M. (2006). OntoNotes: The 90% solution. In R. C. Moore, J. A. Bilmes, J. Chu-Carroll, and M. Sanderson (Eds.), Human language technology conference of the North American chapter of the association of computational linguistics, proceedings, June 4–9. The Association for Computational Linguistics. https://aclanthology.org/N06-2015/.
   Google Scholar
• Ikhwantri, F. (2019). Cross-lingual transfer for distantly supervised and low-resources Indonesian NER. arXiv preprint arXiv:1907.11158.
   Google Scholar
• Jain, A., Paranjape, B., & Lipton, Z. C. (2019). Entity projection via machine translation for cross-lingual NER. In K. Inui, J. Jiang, V. Ng, and X. Wan (Eds.), Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, November 3–7 (pp. 1083–1092). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1100.
   Google Scholar
• Keung, P., Lu, Y., & Bhardwaj, V. (2019). Adversarial learning with contextual embeddings for zero-resource cross-lingual classification and NER. In K. Inui, J. Jiang, V. Ng, and X. Wan (Eds.), Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019 November 3–7 (pp. 1355–1360). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1138.
   Google Scholar
• Kosasih, J. A., & Khodra, M. L. (2018). Transfer learning for Indonesian named entity recognition. In 2018 international symposium on advanced intelligent informatics (sain) (pp. 173–178). https://doi.org/10.1109/SAIN.2018.8673345.
   Google Scholar
• Kurniawan, K., & Louvan, S. (2018). Empirical evaluation of character-based model on neural named-entity recognition in Indonesian conversational texts. In W. Xu, A. Ritter, T. Baldwin, and A. Rahimi (Eds.), Proceedings of the 4th workshop on noisy user-generated text, nut@emnlp 2018, November 1 (pp. 85–92). Association for Computational Linguistics. https://doi.org/10.18653/v1/w18-6112.
   Google Scholar
• Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural architectures for named entity recognition. In K. Knight, A. Nenkova, and O. Rambow (Eds.), NAACL HLT 2016, the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, June 12–17 (pp. 260–270). The Association for Computational Linguistics. https://doi.org/10.18653/v1/n16-1030.
   Google Scholar
• Li, F., Wang, Z., Hui, S. C., Liao, L., Song, D., & Xu, J. (2021). Effective named entity recognition with boundary-aware bidirectional neural networks. In J. Leskovec, M. Grobelnik, M. Najork, J. Tang, and L. Zia (Eds.), WWW '21: The web conference 2021, April 19–23 (pp. 1695–1703). ACM/IW3C2. https://doi.org/10.1145/3442381.3449995.
   Google Scholar
• Li, J., Sun, A., & Ma, Y. (2021). Neural named entity boundary detection. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1790–1795. https://doi.org/10.1109/TKDE.69
   Web of Science ®Google Scholar
• Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020). A unified MRC framework for named entity recognition. In D. Jurafsky, J. Chai, N. Schluter, and J.R. Tetreault (Eds.), Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, July 5–10 (pp. 5849–5859). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.519.
   Google Scholar
• Lin, B. Y., Lee, D., Shen, M., Moreno, R., Huang, X., Shiralkar, P., & Ren, X. (2020). TriggerNER: Learning with entity triggers as explanations for named entity recognition. In D. Jurafsky, J. Chai, N. Schluter, and J.R. Tetreault (Eds.), Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, July 5–10 (pp. 8503–8511). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.752.
   Google Scholar
• Luthfi, A., Distiawan, B., & Manurung, R. (2014). Building an Indonesian named entity recognizer using Wikipedia and DBPedia. In 2014 international conference on Asian language processing, IALP 2014, October 20–22 (pp. 19–22). IEEE. https://doi.org/10.1109/IALP.2014.6973520.
   Google Scholar
• Ma, X., & Hovy, E. H. (2016). End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In Proceedings of the 54th annual meeting of the association for computational linguistics, ACL 2016, August 7–12 (Vol. 1). The Association for Computer Linguistics. https://doi.org/10.18653/v1/p16-1101.
   Google Scholar
• Mayhew, S., Tsai, C., & Roth, D. (2017). Cheap translation for cross-lingual named entity recognition. In M. Palmer, R. Hwa, and S. Riedel (Eds.), Proceedings of the 2017 conference on empirical methods in natural language processing, EMNLP 2017, September 9–11 (pp. 2536–2545). Association for Computational Linguistics. https://doi.org/10.18653/v1/d17-1269.
   Google Scholar
• McCallum, A. (2003). Efficiently inducing features of conditional random fields. In C. Meek and U. Kjærulff (Eds.), UAI '03, proceedings of the 19th conference in uncertainty in artificial intelligence, August 7–10 (pp. 403–410). Morgan Kaufmann.
   Google Scholar
• Menezes, D. S., Milidiú, R., & Savarese, P. (2019). Building a massive corpus for named entity recognition using free open data sources. In 8th Brazilian conference on intelligent systems, BRACIS 2019, October 15–18 (pp. 6–11). IEEE. https://doi.org/10.1109/BRACIS.2019.00011.
   Google Scholar
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Y. Bengio and Y. LeCun (Eds.), 1st international conference on learning representations, ICLR 2013, May 2–4. Workshop Track Proceedings. http://arxiv.org/abs/1301.3781.
   Google Scholar
• Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009). Distant supervision for relation extraction without labeled data. In K. Su, J. Su, and J. Wiebe (Eds.), ACL 2009, proceedings of the 47th annual meeting of the association for computational linguistics and the 4th international joint conference on natural language processing of the AFNLP, August 2–7 (pp. 1003–1011). The Association for Computer Linguistics. https://aclanthology.org/P09-1113/.
   Google Scholar
• Morsidi, F., Sarkawi, S., Sulaiman, S., Mohammad, S. A., & Wahid, R. A. (2015). Malay named entity recognition: A review. Journal of ICT in Education, 2, 1–14. https://ejournal.upsi.edu.my/index.php/JICTIE/article/view/2596
   Google Scholar
• Ni, J., Dinu, G., & Florian, R. (2017). Weakly supervised cross-lingual named entity recognition via effective annotation and representation projection. In R. Barzilay and M. Kan (Eds.), Proceedings of the 55th annual meeting of the association for computational linguistics, ACL 2017, July 30–August 4 (Vol. 1, pp. 1470–1480). Association for Computational Linguistics. https://doi.org/10.18653/v1/P17-1135.
   Google Scholar
• Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In A. Moschitti, B. Pang, and W. Daelemans (Eds.), Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, October 25–29 (pp. 1532–1543). ACL. https://doi.org/10.3115/v1/d14-1162.
   Google Scholar
• Prabhakar, D. K., Dubey, S., Goel, B., & Pal, S. (2014). ISMFIRE-2014: Named entity recognition for Indian languages. In Proceedings of the forum for information retrieval evaluation (pp. 98–102). Association for Computing Machinery. https://doi.org/10.1145/2824864.2824881.
   Google Scholar
• Raiman, J., & Miller, J. (2017). Globally normalized reader. In M. Palmer, R. Hwa, and S. Riedel (Eds.), Proceedings of the 2017 conference on empirical methods in natural language processing, EMNLP 2017, September 9–11 (pp. 1059–1069). Association for Computational Linguistics. https://doi.org/10.18653/v1/d17-1111.
   Google Scholar
• Ranaivo-Malançon, B. (2006). Automatic identification of close languages-case study: Malay and Indonesian. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 2(2), 126–134. https://doi.org/10.37936/ecti-cit.200622.
   Google Scholar
• Salleh, M. S., Asmai, S. A., Basiron, H., & Ahmad, S. (2017). A Malay named entity recognition using conditional random fields. In 2017 5th international conference on information and communication technology (ICOIC7) (pp. 1–6). IEEE. https://doi.org/10.1109/ICoICT.2017.8074647.
   Google Scholar
• Sang, E. F. T. K., & Meulder, F. D. (2003). Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In W. Daelemans and M. Osborne (Eds.), Proceedings of the seventh conference on natural language learning, CONLL 2003, held in cooperation with HLT-NAACL 2003, May 31–June 1 (pp. 142–147). ACL. https://aclanthology.org/W03-0419/.
   Google Scholar
• Sharum, M. Y., Abdullah, M. T., Sulaiman, M. N., Murad, M. A. A., & Hamzah, Z. A. Z. (2011). Name extraction for unstructured Malay text. In 2011 IEEE symposium on computers & informatics (pp. 787–791). IEEE. https://doi.org/10.1109/ISCI.2011.5959017.
   Google Scholar
• Srinivasan, R., & Subalalitha, C. (2019). Automated named entity recognition from tamil documents. In 2019 IEEE 1st international conference on energy, systems and information processing (ICESIP) (pp. 1–5). https://doi.org/10.1109/ICESIP46348.2019.8938383.
   Google Scholar
• Sulaiman, S., Wahid, R., Sarkawi, S., & Omar, N. (2017). Using stanford NER and Illinois NER to detect malay named entity recognition. International Journal of Computer Theory and Engineering, 9(2), 147–150. https://doi.org/10.7763/IJCTE.2017.V9.1128
   Google Scholar
• Ulanganathan, T., Ebrahim, A., Xian, B. C. M., Bouzekri, K., Mahmud, R., & Hoe, O. H. (2017). Benchmarking Mi-NER: Malay entity recognition engine. In 9th international conference on information, process, and knowledge management (pp. 52–58).
   Google Scholar
• Wei, J. W., & Zou, K. (2019). EDA: easy data augmentation techniques for boosting performance on text classification tasks. In K. Inui, J. Jiang, V. Ng, and X. Wan (Eds.), Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019 November 3–7 (pp. 6381–6387). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1670.
   Google Scholar
• Wu, Q., Lin, Z., Karlsson, B., Lou, J., & Huang, B. (2020a). Single-/multi-source cross-lingual NER via teacher-student learning on unlabeled data in target language. In D. Jurafsky, J. Chai, N. Schluter, and J. R. Tetreault (Eds.), Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, July 5–10 (pp. 6505–6514). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.581.
   Google Scholar
• Wu, Q., Lin, Z., Karlsson, B. F., Huang, B., & Lou, J. (2020b). UniTrans: Unifying model transfer and data transfer for cross-lingual named entity recognition with unlabeled data. In C. Bessiere (Ed.), Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI 2020 (pp. 3926–3932). IJCAI.org. https://doi.org/10.24963/ijcai.2020/543.
   Google Scholar
• Wu, S., & Dredze, M. (2019). Beto, Bentz, Becas: The surprising cross-lingual effectiveness of BERT. In K. Inui, J. Jiang, V. Ng, and X. Wan (Eds.), Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, November 3–7 (pp. 833–844). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1077.
   Google Scholar
• Xia, M., Kong, X., Anastasopoulos, A., & Neubig, G. (2019). Generalized data augmentation for low-resource translation. In A. Korhonen, D.R. Traum, and L. Màrquez (Eds.), Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, July 28–August 2 (Vol. 1, pp. 5786–5796). Association for Computational Linguistics. https://doi.org/10.18653/v1/p19-1579.
   Google Scholar
• Zamin, N., & Bakar, Z. A. (2015). Name entity recognition for malay texts using cross-lingual annotation projection approach. In O. Gervasi (Eds.), Computational science and its applications – ICCSA 2015 – 15th international conference proceedings, part I, June 22–25 (Vol. 9155, pp. 242–256). Springer. https://doi.org/10.1007/978-3-319-21404-7_18.
   Google Scholar
• Zamin, N., Oxley, A., & Bakar, Z. A. (2013). Projecting named entity tags from a resource rich language to a resource poor language. Journal of Information and Communication Technology, 12, 121–146. https://e-journal.uum.edu.my/index.php/jict/article/view/8140.
   Google Scholar
• Zhao, S., Liu, T., Zhao, S., & Wang, F. (2019). A neural multi-task learning framework to jointly model medical named entity recognition and normalization. In The thirty-third AAAI conference on artificial intelligence, AAAI 2019, the thirty-first innovative applications of artificial intelligence conference, IAAI 2019, the ninth AAAI symposium on educational advances in artificial intelligence, EAAI 2019, January 27–February 1 (pp. 817–824). AAAI Press. https://doi.org/10.1609/aaai.v33i01.3301817.
   Google Scholar
• Zhao, W., Zhao, S., Chen, S., Weng, T. H., & Kang, W. (2022). Entity and relation collaborative extraction approach based on multi-head attention and gated mechanism. Connection Science, 34(1), 670–686. https://doi.org/10.1080/09540091.2022.2026295
   Web of Science ®Google Scholar
• Zhou, G., & Su, J. (2002). Named entity recognition using an HMM-based chunk tagger. In Proceedings of the 40th annual meeting of the association for computational linguistics, July 6–12 (pp. 473–480). ACL. https://aclanthology.org/P02-1060/.
   Google Scholar
• Zirikly, A. (2015). Cross-lingual transfer of named entity recognizers without parallel corpora. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian federation of natural language processing, ACL 2015, July 26–31 (Vol. 2, pp. 390–396). The Association for Computer Linguistics. https://doi.org/10.3115/v1/p15-2064.
   Google Scholar