{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:11Z","timestamp":1777704911131,"version":"3.51.4"},"reference-count":7,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,8,11]]},"abstract":"<jats:p>Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.<\/jats:p>","DOI":"10.3233\/jifs-202286","type":"journal-article","created":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T12:35:32Z","timestamp":1622550932000},"page":"563-574","source":"Crossref","is-referenced-by-count":4,"title":["Towards corpus and model: Hierarchical structured-attention-based features for Indonesian named entity recognition"],"prefix":"10.1177","volume":"41","author":[{"given":"Yingwen","family":"Fu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nankai","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotian","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-202286_ref4","doi-asserted-by":"crossref","first-page":"136694","DOI":"10.1109\/ACCESS.2019.2942433","article-title":"LSTM-CRF Neural Network with Gated Self Attention for Chinese NER","volume":"7","author":"Jin","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-202286_ref14","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.procs.2016.04.053","article-title":"Indonesian Named-entity Recognition for 15 Classes Using Ensemble Supervised Learning","volume":"81","author":"Wibawa","year":"2016","journal-title":"Procedia Computer Science"},{"key":"10.3233\/JIFS-202286_ref26","first-page":"425","article-title":"Named-Entity Recognition for Indonesian Language using Bidirectional LSTM-CNNs","volume":"135","author":"Gunawan","year":"2018","journal-title":"The 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI)"},{"key":"10.3233\/JIFS-202286_ref32","first-page":"1243","article-title":"Convolutional Sequence to Sequence Learning","volume":"70","author":"Gehring","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning (ICML)"},{"issue":"2","key":"10.3233\/JIFS-202286_ref36","first-page":"282","article-title":"Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data","volume":"3","author":"Lafferty","year":"2001","journal-title":"Proceedings of International Conference on Machine Learning"},{"key":"10.3233\/JIFS-202286_ref37","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1162\/tacl_a_00104","article-title":"Named Entity Recognition with Bidirectional LSTM-CNNs","volume":"4","author":"Chiu","year":"2016","journal-title":"Transactions of the Association for Computational Linguistics"},{"issue":"56","key":"10.3233\/JIFS-202286_ref43","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-202286","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:22Z","timestamp":1777455742000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-202286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":7,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-202286","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,11]]}}}