Named entity recognition for extracting concept in ontology building on Indonesian language using end-to-end bidirectional long short term memory

Santoso, Joan; Setiawan, Esther Irawati; Purwanto, Christian Nathaniel; Yuniarno, Eko Mulyanto; Hariadi, Mochamad; Purnomo, Mauridhi Hery

doi:10.1016/j.eswa.2021.114856

Cited by 31 publications

(14 citation statements)

References 59 publications

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“…Framework Indonesian-open domain information extractor for processing entity-relationship identification, mapping to ontology, and deploying knowledge graphs Indarta et al [24] Extraction of aspects and opinions on lipstick product reviews 591 sentence reviews and 8,574 CRF and HMM Santoso et al [30] Extracting the ontology building concept automatically with NER…”

Section: Slr Resultsmentioning

confidence: 99%

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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%

“…This method can identify known terms and concepts in the unstructured or semi-structured text, but at the same time it also relies on updating. The ontology approach provides additional advantages in terms of making further reasoning and knowledge acquisition for the extracted concepts [23], [30].…”

Section: Bimanlpmentioning

confidence: 99%

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

Budi

Suryono

2023

Bulletin EEI

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“…A substantial amount of effort has been dedicated to recognizing named entities from texts. Early named entity recognition tasks mainly use rule‐ and lexicon‐based methods (Goyal et al., 2018; Rouhou et al., 2021; Santoso et al., 2021). Such approaches depend on hand‐crafted pattern‐matching‐based rules for supporting the recognition and extraction of target entities from geological textual data.…”

Section: Introductionmentioning

confidence: 99%

“…Outside of the geoscience domain, several rule‐based NER techniques/models have been proposed (Appelt et al., 1993; Elsebai et al., 2009; Lehnert et al., 1992). As the amount of data increases, the workload of rule extraction increases, the difficulty of maintaining rule consistency increases, and rule‐based and dictionary‐based methods cannot address the heterogeneity and complexity of text and the thus achieve high GNER performance (Qiu, Xie, Wu, Tao, et al., 2019; Santoso et al., 2021). Compared with rule‐based methods, the statistical learning methods can learn from large amount of annotating training datasets to guide the recognition and extraction NER (Liu et al., 2022; Molina‐Villegas et al., 2021; Peng et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Compared with rule‐based methods, the statistical learning methods can learn from large amount of annotating training datasets to guide the recognition and extraction NER (Liu et al., 2022; Molina‐Villegas et al., 2021; Peng et al., 2021). The popular deep learning methods for named entity recognition generally base on word embeddings, which are able to learn similar representations for semantically or functionally similar words (Fang et al., 2021; Santoso et al., 2021; Tian et al., 2021). Researchers started to construct models without complicated feature engineering and to minimize the reliance on NLP toolkits for feature acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Xie

Xu³

et al. 2022

Earth and Space Science

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Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.

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