Named-Entity Recognition on Indonesian Tweets using Bidirectional LSTM-CRF

Wintaka, Deni Cahya; Bijaksana, Moch Arif; Asror, Ibnu

doi:10.1016/j.procs.2019.08.161

Cited by 25 publications

(21 citation statements)

References 6 publications

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“…The articles were then extracted and mapped according on author, task, Bulletin of Electr Eng & Inf ISSN: 2302-9285  Application of named entity recognition (NER) method for Indonesian datasets: a review (Indra Budi) 973 dataset, and method/technique (see Table 3). It is clear from the table above that several NER studies with Indonesian datasets have been carried out for the following tasks: complaint classification [19], quote identification [9], [20], flood monitoring extraction [7], traffic monitoring [8], [21], tourist [22], zakat [23], lipstick product reviews [24], and various model combination tests for twitter [25]- [28], online news [6], [29]- [31], and Wikipedia [32], [33]. Building a knowledge graph for zakat involves data acquisition, extracting entities and their relationships, mapping to ontologies, and applying knowledge graphs and visualizations.…”

Section: Slr Resultsmentioning

confidence: 99%

Application of named entity recognition method for Indonesian datasets: a review

Budi

Suryono

2023

Bulletin EEI

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A name entity (NE) is a proper name that designates a person, location, or organization. For humans, named entity recognition (NER) is a straightforward process insofar as many named entities are self-names, and most of them have initial capital letters and can be easily recognized, but it is very difficult for machines. This study discusses research trends in the application of NER to Indonesian datasets, particularly as it concerns certain tasks, datasets, methods/techniques, and entity labels. By conducting a systematic literature review (SLR) and bibliometric analysis with VOSviewer, this article hopes to provide opportunities for adopting old methods, combining models from previous research, and even proposing new methods. In addition, the motivation for doing SLR at NER is to look for new strategies in the supervision of financial technology (Fintech). If machines can find illegal Fintech entities on social media and online news, it can help the government to block these illegal Fintech entities. To this end, this study provides an overview of research trends in applying the NER method to Bahasa Indonesia (Indonesian) datasets, including the extraction of news articles, the monitoring of floods, and traffic.

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Section: Slr Resultsmentioning

confidence: 99%

Application of named entity recognition method for Indonesian datasets: a review

Budi

Suryono

2023

Bulletin EEI

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“…Different word embedding models have been investigated in NER research. Many NER studies [18], [33]- [36] have utilized word2vec, including both of its models: Skip-gram and CBOW successfully. Fei et al [37] and Xiaofeng et al [38] used pre-trained Glove model with BLSTM-CRF.…”

Section: Related Workmentioning

confidence: 99%

Classical Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and BERT

Alsaaran

Alrabiah

2021

IEEE Access

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Recurrent Neural Networks (RNNs) and transformers are deep learning models that have achieved remarkable success in several Natural Language Processing (NLP) tasks since they do not rely on handcrafted features nor enormous knowledge resources. Named Entity Recognition (NER) is an essential NLP task that is used in many applications such as information retrieval, question answering, and machine translation. NER aims to locate, extract, and classify named entities into predefined categories such as person, organization and location. Arabic NER is considered a challenging task because of the complexity and the unique characteristics of Arabic. Most of the previous research on deep learning based-Arabic NER focused on Modern Standard Arabic and Dialectal Arabic, which are different variations from Classical Arabic. In this paper, we investigate deep learning-based Classical Arabic NER using different deep neural network architectures and a BERT based contextual language model that is trained on general domain Arabic text. We propose two RNN-based models by fine-tunning the pretrained BERT language model to recognize and classify named entities from Classical Arabic. The pre-trained BERT contextual language model representations were used as input features to a BGRU/BLSTM model and were fine-tuned using a Classical Arabic NER dataset. In addition, we explore variant architectures of the proposed BERT-BGRU/BLSTM-CRF models. Experimentations showed that the BERT-BGRU-CRF model outperformed the other models by achieving an F-measure of 94.76% on the CANERCorpus. To the best of our knowledge, this is the first work that aims to recognize named entities in Classical Arabic using deep learning.

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“…e model structure connected after the input layer is the BiLSTM layer [40,41], where the Chinese character and word vectors from the input layer are fed into the BiLSTM layer together, and the output vectors of this layer based on character and word vectors are spliced and operated in the next step. Finally, they are sent to the CRF layer [42] for final entity recognition. e components of each part are described separately as follows.…”

Section: Model Structurementioning

confidence: 99%

Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion

Hou

Zhang

et al. 2021

Scientific Programming

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The medical information carried in electronic medical records has high clinical research value, and medical named entity recognition is the key to extracting valuable information from large-scale medical texts. At present, most of the studies on Chinese medical named entity recognition are based on character vector model or word vector model. Owing to the complexity and specificity of Chinese text, the existing methods may fail to achieve good performance. In this study, we propose a Chinese medical named entity recognition method that fuses character and word vectors. The method expresses Chinese texts as character vectors and word vectors separately and fuses them in the model for features. The proposed model can effectively avoid the problems of missing character vector information and inaccurate word vector partitioning. On the CCKS 2019 dataset for the named entity recognition task of Chinese electronic medical records, the proposed model achieves good performance and can effectively improve the accuracy of Chinese medical named entity recognition compared with other baseline models.

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Named-Entity Recognition on Indonesian Tweets using Bidirectional LSTM-CRF

Cited by 25 publications

References 6 publications

Application of named entity recognition method for Indonesian datasets: a review

Application of named entity recognition method for Indonesian datasets: a review

Classical Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and BERT

Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion

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