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

Budi, Indra; Suryono, Ryan Randy

doi:10.11591/eei.v12i2.4529

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…4.1 Experimental data ( 9) This paper utilizes the self-constructed dataset SSCchain2023, comprising titles and abstracts related to blockchain. For related work, you can contact the author on github to obtain the author's consent and authorization.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Named entity recognition method of blockchain patent text based on deep learning

Yang,

Niu,

Dai

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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As blockchain technology research continues to advance, named entity recognition within blockchain patent texts has emerged as a fundamental cornerstone for various other tasks. In light of the absence of a publicly accessible corpus within the current realm ofblockchain technology, as well as the challenges posed by insufficient text semantic expression, the dearth of semantic features, and the intricacies associated with capturing diverse word vector representations. This paper constructs a corpus of annotated blockchain patent texts and proposes the MBBC (MacBert-BiLSTM-CRF, MBBC) model for named entity recognition in blockchain patents. The model employs embedding the feature layer to vectorize the input text, enriching text information and mitigating polysemy issues. Text feature information is acquired through the long short-term memory network, and the conditional random field model is integrated to enforce label relationships, yielding an optimal label sequence. In this study, the accuracy rate ofBAT (Blockchain Application Technology, BAT) on the self constructed corpus achieves 91 %, with a recall rate of96% and an Fl value of93%, representing a notable enhancement compared to mainstream models. Ablation experiments further demonstrate that the model in this paper effectively performs named entity recognition tasks associated with blockchain technology and subject content.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Named entity recognition method of blockchain patent text based on deep learning

Yang,

Niu,

Dai

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…It is a challenging task for information retrieval systems. A preliminary step of the NER system involves tokenization, stop word removal, and stemming [25]. The study [26] shows that the stemming can reduce the vocabulary size, which eventually reduces the computation cost and increases the performance of the system.…”

Section: Named Entity Recognition (Ner)mentioning

confidence: 99%

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Jabbar,

Iqbal,

Tamimy

et al. 2023

IEEE Access

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The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.

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“…A unique thing, location, or person is a name entity (NE). Humans can recognize many self-named things with capital letters, but robots struggle with NER [21]. Internet of things (IoT) sensors, data analytics and artificial intelligence (AI), precision agriculture, remote monitoring and control, automated systems, livestock management systems, cloud computing and big data storage, energy management, and farm management software are now in smart farms [22].…”

Section: Introductionmentioning

confidence: 99%

Cloud-based machine learning algorithms for anomalies detection

Amarnath,

Gurulakshmanan

2024

IJEECS

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Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.

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

Cited by 12 publications

References 31 publications

Named entity recognition method of blockchain patent text based on deep learning

Named entity recognition method of blockchain patent text based on deep learning

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Cloud-based machine learning algorithms for anomalies detection

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