named-entity-recognition
794 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
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NuNER
Most implemented papers
Deep Active Learning for Named Entity Recognition
tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
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WS 2017
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition
We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.
VnCoreNLP: A Vietnamese Natural Language Processing Toolkit
We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
A Deep Neural Network Model for the Task of Named Entity Recognition
ZhixiuYe/NER-pytorch
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International Journal of Machine Learning and Computing, Vol. 9, No. 1 2018
One of the most important factors which directly and significantly affects the quality of the neural sequence labeling is the selection and encoding the input features to generate rich semantic and grammatical representation vectors.
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
hackerxiaobai/OpenTag_2019
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We study this problem in the context of product catalogs that often have missing values for many attributes of interest.
Chinese Lexical Analysis with Deep Bi-GRU-CRF Network
baidu/lac
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Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied.
Building a Kannada POS Tagger Using Machine Learning and Neural Network Models
Pruthwik/Training-POS-Chunk-Tagger-Using-BILSTM
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POS Tagging serves as a preliminary task for many NLP applications.
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types.
