Evaluating and applying deep learning-based multilingual named entity recognition

Cheon, Min-Ah; Kim, Chang‐Hyun; Park, Ho-Min; Kim, Jae-Hoon

doi:10.5916/jkosme.2018.42.2.106

Cited by 2 publications

(1 citation statement)

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“…In all datasets, our ''ML → GS'' method outperforms the ''only GS'' method and the results of previous researches ( [5], [47]) based on bidirectional LSTM-CRF. The experiments with Wiki data show that the highest performance improvement in the F1 score was 3.93% point, but all Korean data presents a similar level of performance improvement.…”

Section: ) Results Using Bidirectional Lstm-crf-based Nersupporting

confidence: 53%

Construction of Machine-Labeled Data for Improving Named Entity Recognition by Transfer Learning

Kim

Seo

2020

IEEE Access

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Deep neural networks (DNNs) require a large amount of manually labeled training data to make significant achievements. However, manual labeling is laborious and costly. In this study, we propose a method for automatically generating training data and effectively using the generated data to reduce the labeling cost. The generated data (called ''machine-labeled data'') is generated using a bagging-based bootstrapping approach. However, using the machine-labeled data does not guarantee high performance because of errors in the automatic labeling. In order to reduce the impact of mislabeling, we applied a transfer learning approach. The effect of our proposed method was verified with two versions of DNN-based named entity recognition (NER) models: bidirectional LSTM-CRF and vanilla BERT. We conducted NER tasks in two languages (English and Korean). The proposed method results in average F1 scores of 78.87% (3.9% point improvement) with bidirectional LSTM-CRF and 82.08% (1% point improvement) with BERT on three Korean NER datasets. In English, the performance increased by an average of 0.45% points with the two DNN-based models. The proposed NER systems outperform the baseline systems in both languages without the need for additional manual labeling. INDEX TERMS Named entity recognition, bootstrapping, bagging, transfer learning, deep learning.

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Section: ) Results Using Bidirectional Lstm-crf-based Nersupporting

confidence: 53%