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natural-language-processing-11

Low Resource Named Entity Recognition

3260 papers • 126 benchmarks • 313 datasets

Low resource named entity recognition is the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve named entity recognition tasks in another, commonly more low-resource, language.

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Benchmarks

These leaderboards are used to track progress in low-resource-named-entity-recognition-11

Trend

Dataset

Best Model

Actions

CONLL 2003 German

CONLL 2003 German

Conll 2003 Spanish

Conll 2003 Spanish

CONLL 2003 Dutch

Libraries

i

Use these libraries to find low-resource-named-entity-recognition-11 models and implementations

Datasets

Few-NERD

Broad Twitter Corpus

Broad Twitter Corpus

Rare Diseases Mentions in MIMIC-III

Rare Diseases Mentions in MIMIC-III

Subtasks

No subtasks available.

Most implemented papers

Towards Robust Named Entity Recognition for Historic German

Stefan Schweter, Johannes Baiter•Mon Jun 17 2019

Results show that using SMLM can achieve comparable results for Historic named entity recognition, even when they are only trained on contemporary texts, and it is shown that language model pre-training can be more effective than using transfer-learning with labeled datasets.

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Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

CONLL 2003 Dutch

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Massively Multilingual Transfer for NER

Trevor Cohn, Afshin Rahimi, Yuan Li•Thu Jan 31 2019

Evaluating on named entity recognition, it is shown that the proposed techniques for modulating the transfer are much more effective than strong baselines, including standard ensembling, and the unsupervised method rivals oracle selection of the single best individual model.

228 0

Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels

D. Klakow, Lukas Lange, Michael A. Hedderich•Sun Oct 13 2019

To the best of the knowledge, this approach is the first to leverage feature-dependent noise modeling with pre-initialized confusion matrices and improve upon other confusion-matrix based methods by up to 9%.

15 0

Zero-Resource Cross-Lingual Named Entity Recognition

Shafiq R. Joty, M Saiful Bari, Prathyusha Jwalapuram•Thu Nov 21 2019

This paper proposes an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsuper supervised way without relying on any bilingual dictionary or parallel data.

55 0

Soft Gazetteers for Low-Resource Named Entity Recognition

Graham Neubig, Shruti Rijhwani, Shuyan Zhou, J. Carbonell•Thu Apr 30 2020

This work proposes a method of “soft gazetteers” that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking.

45 0

A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition

Zewen Chi, Heyan Huang, Houjin Yu, Xian-Ling Mao, Wei Wei•Fri Jul 31 2020

This work proposes a novel robust and domain-adaptive approach RDANER for low-resource NER, which only uses cheap and easily obtainable resources, and delivers competitive results against state-of-the-art methods which use difficultly obtainable domainspecific resources.

12 0

ANEA: Distant Supervision for Low-Resource Named Entity Recognition

D. Klakow, Lukas Lange, Michael A. Hedderich•Wed Feb 24 2021

ANA, a tool to automatically annotate named entities in text based on entity lists, spans the whole pipeline from obtaining the lists to analyzing the errors of the distant supervision and it is shown that the F1-score can be increased by on average 18 points through distantly supervised data obtained by ANEA.

23 0

A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition

Yuxuan Chen, Leonhard Hennig, Christoph Alt, Jonas Mikkelsen, Arne Binder•Sun Apr 10 2022

This work introduces an encoder evaluation framework, and uses it to systematically compare the performance of state-of-the-art pre-trained representations on the task of low-resource NER.

3 0

Adding a benchmark result helps the community track progress.

Low Resource Named Entity Recognition | State-of-the-Art