Effective integration of morphological analysis and named entity recognition based on a recurrent neural network

Lee, Hyeon-gu; Park, Geonwoo; Kim, Harksoo

doi:10.1016/j.patrec.2018.08.015

Cited by 17 publications

(4 citation statements)

References 4 publications

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“…Yu and Ko (2016) proposed a morpheme-based NER system using a bidirectional LSTM-CRFs, in which they applied the morpheme-level Korean word embeddings to capture features for morpheme-based NEs on news corpora. Previous works such as Lee et al (2018) and also used the same morpheme-based approach. In these studies, the morphological analysis model was integrated into NER.…”

Section: Previous Workmentioning

confidence: 99%

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Korean named entity recognition based on language-specific features

2023

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In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While the field of NER has been studied extensively in recent years, the mechanism of efficiently recognizing named entities (NEs) in Korean has hardly been explored. This is because the Korean language has distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme for Korean corpora by adopting the CoNLL-U format, which decomposes Korean words into morphemes and reduces the ambiguity of NEs in the original segmentation that may contain functional morphemes such as postpositions and particles, is proposed herein. We investigate how the NE tags are best represented in this morpheme-based scheme and implement an algorithm to convert word-based and syllable-based Korean corpora with NEs into the proposed morpheme-based format. Analyses of the results of traditional and neural models reveal that the proposed morpheme-based format is feasible, and the varied performances of the models under the influence of various additional language-specific features are demonstrated. Extrinsic conditions were also considered to observe the variance of the performances of the proposed models, given different types of data, including the original segmentation and different types of tagging formats.

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Section: Previous Workmentioning

confidence: 99%

“…Previous works such as Lee et al. (2018) and Kim and Kim (2020) also used the same morpheme-based approach. In these studies, the morphological analysis model was integrated into NER.…”

Section: Previous Workmentioning

confidence: 99%

Korean named entity recognition based on language-specific features

2023

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“…BiGRU is an enhanced version of the GRU. It is widely utilized in various domains like optical communication [45], network security [46], structural damage recognition [47], natural language processing [48], etc. However, it is narrowly used in engineering applications, specially in ETD [43].…”

Section: E Electricity Theft Classificationmentioning

confidence: 99%

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

et al. 2022

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Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

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“…σ is the sigmoid function and tanh is hyperbolic tangent function. BGRU is used in [40] for natural language processing.We get our motivation from work in [40] and used BGRU in our proposed model as shown in Figure 8. Bidirectional GRU is the latest version of bidirectional RNN.…”

Section: Bidirectional Gated Recurrent Unit For Classificationmentioning

confidence: 99%

Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters

et al. 2020

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Energy consumption is increasing exponentially with the increase in electronic gadgets. Losses occur during generation, transmission, and distribution. The energy demand leads to increase in electricity theft (ET) in distribution side. Data analysis is the process of assessing the data using different analytical and statistical tools to extract useful information. Fluctuation in energy consumption patterns indicates electricity theft. Utilities bear losses of millions of dollar every year. Hardware-based solutions are considered to be the best; however, the deployment cost of these solutions is high. Software-based solutions are data-driven and cost-effective. We need big data for analysis and artificial intelligence and machine learning techniques. Several solutions have been proposed in existing studies; however, low detection performance and high false positive rate are the major issues. In this paper, we first time employ bidirectional Gated Recurrent Unit for ET detection for classification using real time-series data. We also propose a new scheme, which is a combination of oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE) and undersampling technique Tomek Link: “Smote Over Sampling Tomik Link (SOSTLink) sampling technique”. The Kernel Principal Component Analysis is used for feature extraction. In order to evaluate the proposed model’s performance, five performance metrics are used, including precision, recall, F1-score, Root Mean Square Error (RMSE), and receiver operating characteristic curve. Experiments show that our proposed model outperforms the state-of-the-art techniques: logistic regression, decision tree, random forest, support vector machine, convolutional neural network, long short-term memory, hybrid of multilayer perceptron and convolutional neural network.

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Effective integration of morphological analysis and named entity recognition based on a recurrent neural network

Cited by 17 publications

References 4 publications

Korean named entity recognition based on language-specific features

Korean named entity recognition based on language-specific features

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters

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