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

2019-06-21 · WS 2019 8 · 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.

📄 PDF Abstract BibTeX arXiv:1906.09978

Code (2)

king-menin/slavic-ner pytorch

sberbank-ai/ner-bert pytorch

Tasks

Joint NER and ClassificationLanguage ModelingLanguage ModellingMultilingual Named Entity RecognitionMultilingual text classificationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Residual Connection 설명 없음

Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…

Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.

Weight Decay 설명 없음

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Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Adam 설명 없음

Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation

2019-06-04 · ACL 2019 7 · Benjamin Heinzerling, Michael Strube

Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack …

Multilingual Named Entity Recognition Multilingual NLP named-entity-recognition Named Entity Recognition+2

Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition

2019-08-01 · WS 2019 8 · Genta Indra Winata, Zhaojiang Lin, Pascale Fung

In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings. MME learns to utilize information from these embed…

Language Identification MME named-entity-recognition Named Entity Recognition+1

TLR at BSNLP2019: A Multilingual Named Entity Recognition System

2019-08-01 · WS 2019 8 · Jose G. Moreno, Elvys Linhares Pontes, Mickael Coustaty, Antoine Doucet

This paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed…

Multilingual Named Entity Recognition named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)

Human-Annotated NER Dataset for the Kyrgyz Language

2025-09-23 · Timur Turatali, Anton Alekseev, Gulira Jumalieva, Gulnara Kabaeva 외 arxiv

We introduce KyrgyzNER, the first manually annotated named entity recognition dataset for the Kyrgyz language. Comprising 1,499 news articles from the 24.KG news portal, the dataset contains 10,900 sentences and 39,075 e…

OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages

2024-12-12 · Chester Palen-Michel, Maxwell Pickering, Maya Kruse, Jonne Sälevä 외

We present OpenNER 1.0, a standardized collection of openly available named entity recognition (NER) datasets. OpenNER contains 34 datasets spanning 51 languages, annotated in varying named entity ontologies. We correct …

named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)NER+1