Papers › Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF

Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF

21 Jun 2019WS 2019 8arXiv:1906.09978archive 2025-07-28

Anton A. Emelyanov, Ekaterina Artemova

In this paper we tackle multilingual named entity recognition task. We use the BERT Language Model as embeddings with bidirectional recurrent network, attention, and NCRF on the top. We apply multilingual BERT only as embedder without any fine-tuning. We test out model on the dataset of the BSNLP shared task, which consists of texts in Bulgarian, Czech, Polish and Russian languages.

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Code

king-menin/slavic-ner mentioned on GitHubpytorch report

sberbank-ai/ner-bert mentioned on GitHubpytorchMIT report

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Tasks

Joint NER and Classification Language Modeling Language Modelling Multilingual Named Entity Recognition Multilingual text classification Named Entity Recognition Named Entity Recognition (NER)named-entity-recognition

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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