Low Complexity Named-Entity Recognition for Indonesian Language using BiLSTM-CNNs

Sukardi, Sahrul; Susanty, Meredita; Irawan, Ade; Putra, Randi Fermana

doi:10.1109/icoiact50329.2020.9331989

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…A neural network-based NER model was introduced that used a fixed-size window for each word but did not consider the association information between long text words ( Sun & Li, 2023 ). To address this problem, another study ( Sukardi et al, 2020 ) designed a BiLSTM-CNNs model that could automatically extract text and text-level features. This approach was extended in a subsequent study ( Luboshnikov & Makarov, 2021 ) to a BiLSTM-CNNs-CRF model that added a conditional random field (CRF) layer to optimize the output label sequences.…”

Section: Related Workmentioning

confidence: 99%

Named entity recognition and emotional viewpoint monitoring in online news using artificial intelligence

2024

PeerJ Computer Science

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Network news is an important way for netizens to get social information. Massive news information hinders netizens to get key information. Named entity recognition technology under artificial background can realize the classification of place, date and other information in text information. This article combines named entity recognition and deep learning technology. Specifically, the proposed method introduces an automatic annotation approach for Chinese entity triggers and a Named Entity Recognition (NER) model that can achieve high accuracy with a small number of training data sets. The method jointly trains sentence and trigger vectors through a trigger-matching network, utilizing the trigger vectors as attention queries for subsequent sequence annotation models. Furthermore, the proposed method employs entity labels to effectively recognize neologisms in web news, enabling the customization of the set of sensitive words and the number of words within the set to be detected, as well as extending the web news word sentiment lexicon for sentiment observation. Experimental results demonstrate that the proposed model outperforms the traditional BiLSTM-CRF model, achieving superior performance with only a 20% proportional training data set compared to the 40% proportional training data set required by the conventional model. Moreover, the loss function curve shows that my model exhibits better accuracy and faster convergence speed than the compared model. Finally, my model achieves an average accuracy rate of 97.88% in sentiment viewpoint detection.

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Section: Related Workmentioning

confidence: 99%

Named entity recognition and emotional viewpoint monitoring in online news using artificial intelligence

2024

PeerJ Computer Science

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“…They are used extensively in computer vision, but they are also used to some extent in the field of natural language processing, which is of interest to this study [22]. Layers made consisting of pooling and convolutional layers are used in CNN, which is one of the most selective algorithms.…”

Section: Convolutional Layermentioning

confidence: 99%

A Convolutional Neural Network Model for Setswana Named Entity Recognition

Chabalala,

Owolawi,

Ojo

2023

iCARTi

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Named entity recognition (NER) is a key component of the core task of natural language processing (NLP). In order to represent language, neural networks have been used starting in the 2000s, which enhanced entity recognition outcomes. The Setswana language, in contrast, has never been used with neural networks, in particular convolutional neural networks (CNN). Recently, problems with NLP have been addressed using CNNs, and the results have been quite interesting. CNNs are frequently used in NLP due to their ease of training and reputation as the best in sequence labelling. They depict the interdependence of all conceivable word combinations. Given the difficulties in identifying named entities for South African languages, including Setswana, and the inadequacy of resources, this research proposes the use of CNN model to identify named entities for Setswana. The results obtained are benchmarked with traditional methods such as Conditional random fields (CRF). The performance metrics such as F1-Score are explored in establishing the magnitude of trust and reliability of the proposed model. The model is evaluated using data from the South African Centre for Digital Language Resources' Setswana NER dataset. Compared to the present CRF model, which had an F-score performance of 78.0%, the testing results demonstrate that the model performs 94.0% better.

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“…The Indonesia experiment of NER is not that good compared to English (Gunawan et al, 2018). Recent research was done by (Sukardi et al, 2020) using the neural network combination using BiLSTM and CNN. Indonesia NER researches dominate to recognize the named entity of PERSON, ORGANISATION, LOCATION (Syaifudin & Nurwidyantoro, 2016;Gunawan et.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

Sukmana¹,

Muslimin²,

Firmansyah³

et al. 2021

ASMScJ

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In Indonesia, philanthropy is identical to Zakat. Zakat belongs to a specific domain because it has its characteristics of knowledge. This research studied knowledge graph in the Zakat domain called KGZ which is conducted in Indonesia. This area is still rarely performed, thus it becomes the first knowledge graph for Zakat in Indonesia. It is designed to provide basic knowledge on Zakat and managing the Zakat in Indonesia. There are some issues with building KGZ, firstly, the existing Indonesian named entity recognition (NER) is non-restricted and general-purpose based which data is obtained from a general source like news. Second, there is no dataset for NER in the Zakat domain. We define four steps to build KGZ, involving data acquisition, extracting entities and their relationship, mapping to ontology, and deploying knowledge graphs and visualizations. This research contributed a knowledge graph for Zakat (KGZ) and a building NER model for Zakat, called KGZ-NER. We defined 17 new named entity classes related to Zakat with 272 entities, 169 relationships and provided labelled datasets for KGZ-NER that are publicly accessible. We applied the Indonesian-Open Domain Information Extractor framework to process identifying entities’ relationships. Then designed modeling of information using resources description framework (RDF) to build the knowledge base for KGZ and store it to GraphDB, a product from Ontotext. This NER model has a precision 0.7641, recall 0.4544, and F1-score 0.5655. The increasing data size of KGZ is required to discover all of the knowledge of Zakat and managing Zakat in Indonesia. Moreover, sufficient resources are required in future works.

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Low Complexity Named-Entity Recognition for Indonesian Language using BiLSTM-CNNs

Cited by 7 publications

References 14 publications

Named entity recognition and emotional viewpoint monitoring in online news using artificial intelligence

Named entity recognition and emotional viewpoint monitoring in online news using artificial intelligence

A Convolutional Neural Network Model for Setswana Named Entity Recognition

Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

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